3466 results about "Fast Fourier transform" patented technology

The invention discloses a composite power quality disturbance identification method based on a time-frequency feature fusion classification network. The method comprises the following steps: carrying out noise reduction processing on a noise-containing disturbance signal based on self-adaptive noise complete mode decomposition in combination with a wavelet threshold method; obtaining frequency domain data of the noise reduction signal based on fast Fourier transform; based on a TCN-Transformer deep network, deep feature extraction is carried out on the denoised signals in parallel from a time domain and a frequency domain; and carrying out weighted fusion on the extracted signal time domain and frequency domain features based on a cross attention mechanism to finish classification and identification. The method is simple in principle, the average recognition accuracy rate in a 30dB noise environment can reach 97.86%, and compared with other mainstream deep learning recognition models, the method shows better recognition precision, recognition stability and generalization ability.

The invention provides a service prediction method and device based on a time sequence model, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of historical time sequence data, and obtaining a training data set; performing fast Fourier transform on the training data set to obtain frequency domain data and frequency component intensity; extracting a plurality of main frequency components and corresponding period values from the frequency domain data, and determining a plurality of two-dimensional feature tensors according to the period values and the training data set; constructing L layers of time blocks including feature mapping, an attention mechanism and a feedforward network according to the two-dimensional feature tensor, and establishing residual connection between adjacent time blocks to obtain a time sequence model to be trained; performing feature extraction on each two-dimensional feature tensor by applying a multi-branch convolutional network to obtain a one-dimensional representation vector, and performing weighted fusion on the one-dimensional representation vector according to the frequency component intensity to obtain a fusion feature; and training the to-be-trained time sequence model through the fusion features, and outputting an analysis report of historical time sequence data.

The invention provides a chip welding method based on machine vision and control, and the method comprises the steps: obtaining a three-dimensional model of a chip and a substrate, obtaining an initial relative position of a chip welding spot and a substrate welding pad, and generating a mechanical arm initial motion instruction; images of the chip and the substrate are shot, pose deviation is calculated through image registration, if the deviation exceeds a threshold value, a compensation instruction is generated to be fused with an initial instruction, and the track of a mechanical arm is adjusted to be aligned with a bonding pad; a temperature sensor array is constructed to monitor the temperature of the welding area, the average temperature is calculated, and if a difference exists between the average temperature and the ideal welding temperature interval, the heater power is adjusted through a proportional integral differential algorithm; a piezoelectric sensor is used for obtaining a mechanical arm joint vibration signal, the dominant vibration frequency and amplitude are extracted through fast Fourier transform, and a piezoelectric actuator is used for generating an anti-phase driving signal to counteract vibration.

The invention discloses a liquid crystal display screen defect detection method, and relates to the technical field of liquid crystal display screen defect detection.A bidirectional polarization transmission structure light source is adopted to project 0-degree and 90-degree polarized light, interference of parasitic reflection on defect detection is reduced, and a self-adaptive exposure control unit is introduced; exposure parameters are adjusted in real time according to the light transmittance and reflection characteristics of the glass substrate, the imaging quality is optimized, and the detection effect is prevented from being affected by overexposure or underexposure; in the image processing stage, parasitic reflection information is extracted by combining a polarization difference algorithm of double-frame subtraction, high-frequency interference is removed by using fast Fourier transform (FFT), an optimized defect image after parasitic reflection suppression is obtained, real defect information is more prominent, and the defect contrast is improved; multi-scale Gaussian fitting filtering, a gradient lifting method and super-resolution reconstruction are adopted to enhance defect features in the image, so that a low-contrast defect area is easier to identify.

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 relates to the technical field of signal acquisition, and discloses a signal acquisition method and system for an MEMS gas sensor array, and the method comprises the steps: carrying out the analog-to-digital conversion and temperature and humidity compensation of resistance value signals synchronously acquired by a plurality of sensing units in the MEMS gas sensor array, and obtaining a compensated response matrix; performing fast Fourier transform and dynamic feature extraction to obtain a frequency domain feature matrix of each sensing unit; performing sensing unit crosstalk coefficient calculation and response component decoupling to obtain a decoupling feature vector; carrying out Hilbert transform and adsorption-desorption characteristic extraction to obtain a gas sensitive characteristic sequence; inputting a self-adaptive power spectrum analysis model to carry out cross sensitivity identification processing to obtain a target gas response component; according to the method, the accuracy of target gas detection and concentration quantitative analysis is improved.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

The invention discloses a virtual synchronous generator grid-connected current harmonic suppression method and system, and relates to the technical field of power systems, and the method comprises the steps: S1, carrying out the digital sampling of intermittent high-frequency disturbance through a wind power plant grid-connected current signal in a power grid through an analog-to-digital converter, and obtaining an original digital current sequence; s2, according to the original digital current sequence, analyzing a wind load frequency component by adopting a fast Fourier transform algorithm, and determining amplitude and phase characteristics of a high-frequency harmonic component; s3, if the amplitude of the high-frequency harmonic component exceeds a preset threshold value, extracting an intermittent disturbance harmonic sequence from the frequency component to obtain a to-be-suppressed harmonic subset; according to the virtual synchronous generator grid-connected current harmonic suppression method and system, the stability of a remote wind power plant power grid is remarkably improved, continuous monitoring and real-time suppression of intermittent disturbance are achieved, and high efficiency and reliability of power grid operation are guaranteed.

The invention discloses a bearing fault diagnosis method based on a parallel multi-fusion deep learning network model. And obtaining a sample data set, dividing the sample data set into a training data set and a test data set, carrying out parallel multi-fusion deep learning network model training by using the training data set, and inputting the test data set into the trained parallel multi-fusion deep learning network model to carry out fault diagnosis. Wherein the parallel multi-fusion deep learning network model comprises three parallel sub-networks, namely a bidirectional gating circulation unit sub-network, a fast Fourier transform-convolutional neural network sub-network and a discrete wavelet transform-enhanced depth separable ion network, and a dynamic multi-head attention mechanism module. According to the method, the three parallel sub-networks and the dynamic multi-head attention mechanism module are combined to automatically extract and identify the fault features of the rolling bearing under multiple working conditions, and self-adaptive fault feature extraction under different domain signals and rolling bearing fault diagnosis under complex working conditions are realized.

The invention discloses a glass ceramic thickness detection method and a related device, and the method comprises the steps: obtaining a vibration signal generated in a glass ceramic thinning and grinding process, and extracting a vibration dominant frequency, a frequency spectrum centroid frequency and a frequency band energy distribution ratio as thickness characteristic parameters through fast Fourier transform processing; three-dimensional position coordinates of the grinding head are obtained, space mapping and weighted average processing are conducted on the thickness characteristic parameters, and space distribution of the thickness characteristic parameters is obtained; performing process interference compensation and sliding window time sequence smoothing processing according to the grinding process parameters to obtain stable thickness characteristic distribution; based on the stable thickness feature distribution and the current grinding process, final thickness distribution is predicted, and a thickness qualification pre-judgment result is obtained; and extracting a time sequence change characteristic of the thickness characteristic parameter as a vibration evolution index, and performing comprehensive quality evaluation in combination with a thickness eligibility pre-judgment result. According to the scheme, the thickness can be detected in real time in the thinning and grinding process of the microcrystalline glass, and the production efficiency is improved.

The invention discloses a three-dimensional signal secure transmission method and device based on index modulation. The method comprises the following steps: generating four-dimensional chaotic sequences X, Y, Z and U from a key initial value through a chaotic model; performing XOR operation on the original data by using an encrypted sequence generated by X and U in the four-dimensional chaotic sequence to obtain encrypted data; performing binary conversion on the key initial value to obtain a binary key initial value consisting of N four bits; dividing the encrypted data into N groups, each group having 15 subcarriers, determining a to-be-inserted silent position of each group of subcarriers according to a binary key initial value, performing three-dimensional mapping after the silent subcarriers are inserted, and setting the silent subcarriers at an original point position to obtain a three-dimensional constellation diagram; performing constellation masking on the three-dimensional constellation diagram by using Y and Z in the four-dimensional chaos sequence; carrying out IFFT (Inverse Fast Fourier Transform) on the three-dimensional constellation diagram after constellation masking, adding a suffix, and carrying out parallel-serial conversion to obtain transmission data; and transmitting the transmission data to a receiving end through an optical fiber channel.

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention provides a time sequence prediction method and system for a KAN enabling graph attention parallel loop network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining time sequence input data; inputting the time sequence input data into a feature extraction layer based on fast Fourier transform and discrete wavelet transform, and preprocessing the time sequence input data by the feature extraction layer to obtain spliced feature extraction data; inputting the spliced feature extraction data into a feature aggregation layer of graph attention based on KAN, and preprocessing the spliced feature extraction data by the feature aggregation layer to obtain feature aggregation data; and inputting the feature aggregation data into a time sequence prediction model of the parallel gating circulation unit network based on KAN and attention, and outputting time sequence prediction data by the time sequence prediction model. According to the method, the calculation complexity of time sequence prediction is reduced, and the time sequence prediction efficiency and accuracy are improved.

The invention discloses a visual time sequence feature network implementation method based on multi-module feature fusion. The system comprises a visual time sequence feature encoder VTFE, a multi-scale self-attention fusion module MSAF, an efficient feature reconstruction engine EFRE and an adaptive feature refinement processor AFRP. The VTFE module fuses a convolutional neural network and an extended long-short-term memory network, and realizes high-precision capture of spatial-temporal features. The MSAF module processes characteristic frequency domain information through fast Fourier transform, and the recognition capability of complex boundaries and noise is enhanced. The EFRE module optimizes feature reconstruction through depth separable convolution and jump connection to maintain global consistency. The AFRP module integrates multi-level features and improves the detail expression ability through deep convolution and an attention mechanism. The method is excellent in performance on a large-scale OCT data set, the segmentation precision of the retina edema and the recognition capability of a fine structure are remarkably improved, and an efficient and non-invasive auxiliary tool is provided for early diagnosis of ophthalmic diseases.

The invention discloses a DC voltage divider parameter anomaly detection method based on harmonic characteristics, and the method comprises the steps: deploying an isolation type voltage sensor at a bus of a DC power transmission system, carrying out the voltage sampling through combining with an ADC, carrying out the data buffering and preprocessing, extracting a fundamental wave component and a target harmonic component through the fast Fourier transform, and calculating the harmonic distortion rate. And constructing a low-voltage arm capacitance and harmonic distortion rate model based on a voltage divider secondary circuit equivalent circuit and an energy distribution principle, and calibrating the low-voltage arm capacitance and harmonic distortion rate model. And designing a multi-objective optimization scheduling algorithm based on energy storage capacity and capacitance characteristics, and realizing dynamic power distribution and harmonic suppression of the system. And after abnormal triggering, the system automatically loads a capacitance compensation coefficient, adaptively adjusts algorithm parameters in combination with a measurement loop PI control link, reduces a harmonic amplification effect, and ensures that a measurement error is within an acceptable range. Harmonic monitoring precision and capacitance compensation optimization capability of the direct-current power transmission system are effectively improved, and stable operation of the system is guaranteed.

The invention provides a bearing fault diagnosis method and system, a storage medium and a computer, and the method comprises the steps: obtaining a bearing vibration signal, and extracting time-frequency features in the bearing vibration signal according to variational mode decomposition and fast Fourier transform; performing convolution operation on the time-frequency features, and processing a convolution output result by adopting an ECA attention mechanism to extract frequency domain features in the time-frequency features; capturing a global time sequence dependency relationship in the time-frequency features based on a Transform model to extract long-distance dependent time domain features in the time-frequency features; and fusing the extracted frequency domain features and time domain features through a bidirectional cross attention mechanism, transmitting the fused features into a full connection layer for classification, outputting fault categories of the bearing, converting a classification result into probability distribution, and selecting the category with the highest probability as a diagnosis result. The bearing fault diagnosis method provided by the invention is high in fault diagnosis accuracy.

The invention relates to a transformer multi-physics field coupling numerical simulation method and device and a storage medium. The method comprises the following steps: performing coupling calculation on a target working condition by using an electromagnetic, heat, flow and force coupling calculation model of a transformer to obtain transformer loss data and temperature rise data; mapping the temperature rise data to a transformer electromagnetic and force coupling calculation model by using a grid node data mapping method to serve as an initial value, and performing coupling calculation on a target working condition to obtain dynamic data of an internal structural member of the transformer; performing fast Fourier transform on the dynamic data in a preset frequency range to obtain dynamic data under each frequency; and mapping the temperature rise data and the dynamic data under each frequency to a transformer sound and force coupling calculation model as initial values, and carrying out coupling calculation on a target working condition to obtain transformer oil tank vibration and external noise data. Compared with the prior art, the method has the advantages that the calculation amount is effectively reduced, and the accuracy of predicting the internal state of the transformer is improved.

The invention discloses an adaptive modulation anti-multipath unmanned aerial vehicle swarm communication method and system based on MIMO-OFDM, and relates to the technical field of wireless communication, and the method comprises the steps: receiving a down-conversion baseband; synchronization and compensation are carried out; removing a cyclic prefix and carrying out fast Fourier transform; estimating a channel and noise on the training and pilot frequency, and correcting a common phase; performing frequency domain equalization and soft decision, forward error correction and cyclic redundancy check according to the estimation to obtain a control parameter, decoding a payload according to the control parameter, and performing statistics to form a link quality indication; modulating coding, bandwidth and spatial mode, configuring mapping, power and weight, and packaging physical layer protocol data; carrier sensing conflict avoiding sending, confirmation and retry are adopted to generate media access statistics; and the routing selects the next-hop multi-hop forwarding according to the transmission quality, and performs end-to-end statistics and recharge. Through cooperation of hierarchical synchronization and phase correction with cyclic prefix and FFT, crosstalk is suppressed, channel noise scale optimization soft decision is robust, data decoupling is controlled, and end-to-end availability is improved through multi-hop self-healing recharge.

A system includes a first device connected to a power line and a second device connected to a circuit breaker switch upstream of the first device. A transmitter of the first device communicates a signal pertaining to amplitude, phase, frequency, switching frequency and duty cycle of current, including multiple current signatures that include an unique first current signature from the first device and at least one second current signature associated with one or more load fixtures on the power line, with a receiver of the second device. A processor in communication with the receiver processes the signal using Fast Fourier Transform (FFT) to obtain the unique first current signature and the at least one second current signature, perform line and load mapping, and generate user intelligible data representation of the confirmation and determination.

The invention requests to protect a rotating machine noise robust fault diagnosis method based on a mixed domain graph neural network. Comprising the following steps: collecting vibration data, and dividing an original vibration signal into a plurality of sections of subsamples with the length of 1024 by using non-overlapping sampling; converting the constructed subsample signal vibration signal into a frequency domain signal by using fast Fourier transform (FFT); taking the subsample frequency domain signals as nodes, the frequency spectrums as node features and the fault types as node labels, and then constructing the nodes into a graph structure; dividing the generated graph structure data set into a training set and a test set; and performing fault diagnosis on a test set by using the trained model, realizing self-adaptive weight adjustment by using a gating mechanism to fuse mechanical features, outputting a diagnosis result, calculating a performance index and performing result visualization analysis. The result shows that compared with a single-domain graph neural network, the method has higher accuracy and noise immunity, and end-to-end rotating machine fault diagnosis under the strong noise condition can be effectively achieved.

The invention relates to an audio feature extraction method and device based on a neural network. The method comprises the following steps: performing framing processing and fast Fourier transform on an original audio signal acquired by a vehicle-mounted microphone to obtain a frequency domain feature sequence; extracting short-time energy, a zero-crossing rate, a frequency spectrum centroid and a Mel-frequency cepstrum coefficient from the frequency domain feature sequence, and combining first-order difference and second-order difference features of each feature to obtain a multi-dimensional acoustic feature point sequence; performing time sequence arrangement on the multi-dimensional acoustic feature point sequence, constructing an original feature matrix, and performing principal component analysis on the original feature matrix to obtain a feature description matrix; inputting the feature description matrix into a dynamic time feature extraction network for deep time-frequency feature analysis to obtain vehicle-mounted voice deep time-frequency features; and performing voice detection classification based on the vehicle-mounted voice deep time-frequency features, and outputting a vehicle-mounted voice detection result. According to the invention, various noise interferences in a vehicle-mounted environment are effectively suppressed, and the anti-noise capability of the system is enhanced.

The invention discloses a temperature collecting, monitoring and alarming method for palm fire fighting, relates to the technical field of temperature monitoring for fire fighting, and is used for solving the problem of inaccurate temperature monitoring and prediction. According to the method, environmental factor data are synchronously collected through temperature monitoring points, the correlation between each factor and the temperature is calculated after denoising, multi-factor characteristics are extracted to generate an output characteristic vector of temperature prediction, the output characteristic vector is constructed into a time sequence matrix to be input into a long-short-term memory network, and temperature trend characteristics are extracted; the method comprises the following steps: predicting a future trend and comparing the future trend with a safety threshold to judge whether early warning is triggered or not, meanwhile, identifying temperature changes of different frequencies by adopting fast Fourier transform, a weighted moving average method and an exponential smoothing method through short-term, medium-term and long-term scale temperature trend analysis, setting a three-level early warning mechanism and dynamically adjusting the threshold, and finally, carrying out early warning. Based on early warning feedback data, a Q-learning algorithm is adopted to optimize a threshold value and an alarm strategy, a self-adaptive learning closed loop is formed, and the early fire early warning capability is improved.

The invention relates to the technical field of noise filtering, in particular to a communication line noise filtering system and method, and the system comprises a temperature coupling module, a voltage response module, a parameter joint debugging module and a link checking module. According to the method, inductance and capacitance node temperature data are collected and input into a piecewise linear regression model, the temperature-to-resonant frequency deviation gradient is calculated, a noise suppression bandwidth threshold value is dynamically matched in combination with a reference table, the frequency stability under temperature fluctuation is improved, Kalman filtering is used for processing voltage sampling, and a fluctuation predicted value is generated. According to the method, interval indexes are extracted based on a parameter mapping table, instant inductance and capacitance parameters are generated through least square fitting, voltage transient fluctuation self-adaption is achieved, noise power spectrum density is calculated through fast Fourier transform, a 3dB attenuation point is compared with a threshold value difference value to trigger parameter rollback, closed-loop control is formed, it is ensured that suppression bandwidth is continuously matched with a noise frequency band, and noise suppression is achieved. The bandwidth offset is reduced to be within 1.2%, and the optimization response time is shortened to 200 ms level.

The invention belongs to the technical field of bridge health monitoring, and aims at solving the problem that a traditional real-time cable force calculation method is low in recognition precision. The invention provides an inhaul cable real-time cable force identification method and device based on deep learning. The method comprises the following steps that vibration image information of a target inhaul cable is acquired; obtaining a target image sequence based on the vibration image information; adopting an optical flow estimation algorithm based on deep learning to obtain vibration displacement of a target inhaul cable in the target image sequence; variational mode decomposition transformation is conducted on the vibration displacement, and the mode component and the real-time frequency of the target inhaul cable are obtained; performing fast Fourier transform on the modal component, and determining the inherent frequency of each order and the corresponding order of the target inhaul cable; and calculating the real-time cable force of the target cable based on the real-time frequency and the order of the inherent frequency corresponding to the real-time frequency. According to the method, the vibration displacement identification precision of the target inhaul cable is improved, and the robustness of real-time cable force identification is improved.

The invention relates to the technical field of road surface detection, in particular to a road surface skid resistance detection system based on multi-feature fusion, which comprises the following steps: applying broadband sweep frequency excitation by using a frequency modulation vibration sensor, matching with the inherent frequency of road surface texture to generate local resonance, and collecting the temperature, humidity and rainfall of a road surface in real time; performing fast Fourier transform on the collected vibration signals to obtain a resonance response spectrum, extracting key parameters through Gaussian fitting, and fusing frequency domain, material and environment data to form a comprehensive feature set; the frequency domain features are converted into three-dimensional energy distribution of pavement microtextures, the actual contact area ratio is calculated according to the three-dimensional energy distribution, an environment temperature and humidity compensation factor is introduced, and a dynamic friction attenuation coefficient is calculated; and comparing the calculated dynamic friction attenuation coefficient with a third-level safety threshold, and outputting a corresponding anti-skid performance level. Multi-source data fusion enables a detection result to be more fit with an actual driving scene, and misjudgment caused by single data is avoided.

The present invention discloses a method and system for intelligent control of generator output, which relates to the field of intelligent power control, including collecting generator operation data, sampling the generator output waveform, and extracting harmonic distortion data using fast Fourier transform; based on the generator operation data, predicting future changes through a long short-term memory network model to obtain load change trends; according to the load change trend, combined with harmonic distortion data, identifying the correlation between harmonic sources and loads, and generating a harmonic compensation scheme; based on the harmonic compensation scheme and the load change trend, generating a dual control strategy to adjust the generator operating parameters. The present invention generates a dual control strategy by combining the harmonic compensation scheme with the load change trend, thereby realizing dynamic adjustment of the generator output power, reactive power, excitation current and speed, and optimizing the generator operating parameters and energy efficiency.

The invention discloses a photovoltaic grid-connected harmonic current prediction method based on deep learning, and relates to the technical field of power systems and photovoltaic grid connection. According to the method, three-phase load current and grid-connected point voltage data in a photovoltaic grid-connected system are obtained, the three-phase load current is converted into a two-phase direct-current component through Clark and Park conversion, fundamental current is extracted through a low-pass filter, and a harmonic current component is obtained. Time domain and frequency domain characteristics of grid-connected point voltage data are extracted through fast Fourier transform, correlation between voltage harmonic waves and load current harmonic waves is analyzed, and grid-connected point voltage characteristics are extracted. Local features of harmonic current components are extracted through a convolutional neural network, time sequence features are captured through a bidirectional long-short-term memory network, and a harmonic current prediction model is constructed. And finally, inputting the preprocessed current and voltage characteristics, and generating a harmonic current predicted value. And the problems of nonlinearity and uncertainty in the photovoltaic grid-connected system can be more effectively solved.

The invention relates to a CNN model-based power cable image identification and labeling method and system, and the method comprises the steps: collecting a basic image and three-dimensional point cloud data of a power line, and carrying out the fusion of the basic image and the three-dimensional point cloud data, and obtaining a fusion image; performing target detection on the fused image based on a CNN network to obtain a target area image including cable defects; performing FFT (Fast Fourier Transform) on the target area image to obtain a texture enhanced image; and establishing an SVM model, and carrying out cable fault classification based on cable defects through the target area image and the texture enhancement image. According to the method, the detection and classification work of the power cables is completed in combination with the texture features, the power cable faults are accurately classified, and the detection efficiency and precision are greatly improved.

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

The invention provides a fumigation control method based on pest and disease damage trend prediction, and the method comprises the steps: carrying out the spectrum analysis of audio data information through employing a fast Fourier transform algorithm, extracting the frequency domain feature vector of the chirp sound of each pest species, and enabling the frequency domain feature vector to comprise a fundamental frequency, harmonic energy distribution, chirp duration and chirp rhythm; according to the extracted chirp frequency domain feature vector, chirps of local main agricultural pests are identified and classified by adopting a support vector machine algorithm, and the types and life cycle information of the pests are judged by matching with a preset pest species chirp feature template library; and performing spatial mapping on the predicted population density distribution data by adopting a Kriging interpolation algorithm to generate a spatial distribution diagram, determining the aggregation degree and diffusion trend of pests in each region of the field, and dividing a plurality of prevention and control regions and buffer regions.