168 results about "Analytic signal" patented technology

The invention provides a directional energy multi-target interference system based on an ultra-wideband phased array technology, and the system integrates signal perception, target recognition, interference signal generation and phased array control, so as to achieve the precise interference of an unmanned aerial vehicle cluster. The signal sensing module scans an airspace target through the ultra-wideband phased-array antenna array and obtains spectrum characteristics of a target signal. The target identification module analyzes signal features, identifies an individual target and predicts a motion track and a communication protocol of the individual target. And the phased array control module calculates and adjusts antenna beam forming parameters, and dynamically controls the radiation direction, phase and transmitting power of the antenna. The interference signal generation module generates a matched interference waveform based on the target communication protocol and the trajectory information. The ultra-wideband phased-array antenna array adopts a digital beam forming technology. According to the invention, efficient and accurate directional energy interference can be realized under multiple frequency bands, the confrontation capability of the unmanned aerial vehicle cluster is improved, and the method is suitable for defense tasks in electronic warfare and complex electromagnetic environments.

The invention discloses a multi-signal quality monitoring system and method in the numerical control machining process, and belongs to the technical field of numerical control machining. The method comprises the following steps: step 1, synchronously acquiring multi-source signals in a microscopic level, a mesoscopic level and a macroscopic level in a processing process; 2, screening to form an optimal feature subset, and constructing a multi-dimensional feature representation space; 3, constructing a same-scale transverse incidence matrix, a cross-scale longitudinal incidence matrix and an interaction incidence matrix based on a bidirectional attention mechanism, and forming an incidence network structure for describing feature coupling and dependency relationships among signals of different levels; step 4, calculating deviation metric values of various signal features, identifying an abnormal diffusion path in combination with a topological structure of the association network, and analyzing a causal relationship chain between the signals; and step 5, matching the identified abnormal mode with a pre-constructed process knowledge graph, automatically generating a targeted process parameter optimization suggestion, and performing real-time evaluation to realize closed-loop adaptive control for quality abnormity.

The invention relates to the technical field of trapping equipment control, and discloses a trapping equipment cooperative control system based on multi-sensor fusion, which comprises a multi-source data acquisition module, a target identification module, a risk assessment module, a cooperative relationship modeling module, a path optimization module, a decision fusion module and an execution control module. The multi-source data acquisition module generates an environment perception map containing temperature, humidity and illumination; the target recognition module analyzes the signals and generates data containing the target size and the motion category in a classified mode; the risk assessment module divides risk areas and inverts threats to generate a risk distribution map; the collaborative relation modeling module extracts collaborative parameters such as communication delay; the path optimization module dynamically plans a path; the decision fusion module performs weighted fusion on the data generation indexes; and the execution control module matches actions and adjusts parameters. The system realizes multi-dimensional perception, accurate identification and cooperative control, improves the trapping efficiency, and is suitable for pest control and other scenes.

The invention relates to the technical field of Internet of Things communication, and provides a low-power-consumption semiconductor communication method based on Internet of Things, which comprises the following steps: acquiring temperature, electromagnetic interference intensity and signal attenuation coefficient data through a multi-mode sensor node, and generating an original environment data set; performing dynamic noise classification on the original environment data set, dividing noise levels and marking noise types; performing hierarchical processing on the data by adopting a multi-stage self-adaptive noise reduction algorithm to generate a preprocessed data set; extracting dynamic characteristic parameters of a communication channel, constructing an energy efficiency prediction model, and dynamically calculating a combination of optimal transmitting frequency, power and modulation order; generating a multi-system modulation signal, superposing a dynamic coding strategy, and adjusting the symbol block length and the error correction code density; and the receiving end analyzes the signal quality index, feeds back the signal quality index to the transmitting end to optimize the parameters and updates the energy efficiency prediction model. According to the invention, the energy efficiency ratio and the anti-interference capability of the Internet of Things communication system can be improved.

The invention belongs to the technical field of power equipment state monitoring and fault diagnosis, and discloses a high-voltage circuit breaker state quantity feature extraction method and system, and the method comprises the steps: extracting a local maximum value point and a local minimum value point from a denoised original monitoring signal; feature vectors are constructed and input into the trained neural network model, six-dimensional vectors are output in a Softmax output layer, and the six-dimensional vectors correspond to prediction probabilities of six orders in the optimal B-spline interpolation orders; based on the constructed envelope, adopting empirical mode decomposition to obtain a plurality of intrinsic mode function IMF components, performing Hilbert transform on each IMF component, constructing a state quantity feature set according to the instantaneous frequency and amplitude change of the analysis signal, and judging whether the high-voltage circuit breaker is in an abnormal state or not. According to the method, the vibration signals collected by the circuit breaker are subjected to multi-stage intelligent processing, high-precision and low-noise feature extraction is achieved, and fault diagnosis and state recognition of the circuit breaker can be effectively supported.

The invention relates to the technical field of electrical engineering and intelligent fault diagnosis, and discloses a water pump motor fault diagnosis method and system based on current harmonic characteristics, and the method comprises the steps: collecting stator current, rotating speed and load signals; generating an alpha-beta current component through Clark transformation, constructing an analytic signal, extracting an instantaneous phase, and calculating an instantaneous frequency; a mapping relation is established in combination with the rotating speed, and the current signals are resampled to eliminate frequency dispersion; obtaining a high-resolution harmonic energy distribution diagram by adopting multi-scale synchronous compression wavelet transform; and extracting an energy concentration ratio, a peak offset and a bandwidth expansion coefficient in the characteristic frequency band, inputting the energy concentration ratio, the peak offset and the bandwidth expansion coefficient into the pre-trained multi-layer perceptron classifier, and outputting a fault type. According to the method, through high-precision time-frequency focusing and multi-dimensional feature fusion, the fault recognition accuracy and robustness are remarkably improved, and online diagnosis of multiple typical faults can be achieved only through current signals.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

The invention relates to the field of artificial intelligence, and discloses a brain-like computing-driven visual information instant analysis method and system, which comprises the following steps of: firstly obtaining real-time perception data and a neural pulse signal, analyzing a signal perception curve to construct a brain-like computing network, then identifying visual information characteristics through the network, analyzing a preset model computing architecture, and obtaining a brain-like computing network; optimizing a neuron mapping relation to obtain an optimized mapping network, analyzing a path based on the analysis information, determining a time sequence distribution state of target data, calculating an analysis efficiency value, determining an analysis optimization direction, generating an instant analysis strategy, calculating an analysis attenuation rate, determining an analysis state according to the attenuation rate, analyzing an analysis dimension, and finally constructing an optimization analysis scheme. According to the invention, the intelligent analysis efficiency of the visual information can be improved.

The invention discloses a real-time wave number space resampling method and system for SS-OCT, and relates to the technical field of signal processing, and the method comprises the steps: synchronously collecting a K clock signal and an OCT interference signal, and carrying out the filtering preprocessing of the signals; carrying out Hilbert transformation on the preprocessed K clock signal, constructing an analysis signal, extracting an instantaneous phase, and forming an instantaneous phase sequence; using a preset ideal equal wave number interval phase sequence as a target phase sequence, matching the target phase sequence with the instantaneous phase sequence, calculating a corresponding position index of each target phase in the instantaneous phase sequence, and obtaining a remapping sampling point index; and processing the preprocessed OCT interference signal by using an SINC interpolation method according to the remapping sampling point index to obtain an OCT signal which is subjected to resampling and has equal wave number intervals. According to the invention, the SINC interpolation algorithm is utilized to complete resampling in real time and with high precision in the data acquisition stage, and the requirement of high-speed real-time imaging is met.

The invention provides a method and equipment for improving the precision of pole-shifting proximity real-time prediction by fusing time-varying characteristics, and the method comprises the steps: carrying out the data preprocessing of a pole-shifting PM, and extracting a trend term, a period term and a residual term; constructing a Prony method-based PM time-varying characteristic fused least square model, and performing dynamic parameter estimation and extrapolation prediction on the period term and the trend term; constructing a polar shift residual prediction model based on ARIMA (autoregressive integrated moving average), and predicting the residual item; and reconstructing the prediction results of the period term, the trend term and the residual term to generate a polar shift prediction result. According to the method, the amplitude, damping, period and phase change in the PM signal can be independently and effectively analyzed, and then the corresponding prediction model is constructed, so that the precision of near-real-time PM prediction is improved.

The invention relates to an equipment fault diagnosis method and system. The method comprises the following steps: performing signal decomposition on an original vibration signal to obtain a group of intrinsic mode components; for each intrinsic mode component, calculating a kurtosis value and an order cyclic spectral density amplitude; based on the kurtosis value and the order cyclic spectral density amplitude, constructing a frequency band selection index used for representing fault impact energy and periodical modulation certainty; determining one or more intrinsic mode components enabling the frequency band selection index to be optimized, and determining an optimal filtering frequency band according to the center frequency and the bandwidth of the optimized intrinsic mode components; performing Hilbert transform on the filtered signal to obtain an analytic signal; extracting the instantaneous amplitude of the analysis signal to obtain an envelope signal; performing cepstrum editing on the envelope signal to obtain an enhanced envelope signal, and performing fast Fourier transform on the enhanced envelope signal to obtain an enhanced envelope spectrum; and performing fault diagnosis based on the fault characteristic frequency in the enhanced envelope spectrum.

The invention discloses a magnetic target boundary recognition method and system based on a magnetic gradient tensor, and relates to the technical field of geophysical exploration and industrial nondestructive testing, and the method comprises the following steps: collecting spatial magnetic gradient tensor data through a magnetic gradient sensor array, and obtaining nine components of the magnetic gradient tensor; calculating the vertical guide modulus of the vertical component of the magnetic gradient tensor; calculating a Z-direction analysis signal ASAz and a vertical guide thereof; a boundary recognition amount is generated by a ratio operation, and a boundary position of the magnetic target is determined based on a maximum value position of the boundary recognition amount. According to the method, a vertical component derivative and analytic signal derivative dual-derivative coupling mechanism is combined for the first time, the limitation that a traditional method depends on a single parameter is overcome, the vertical component derivative accurately captures the change rate of the boundary in the vertical direction, and the analytic signal derivative effectively suppresses background noise.

The invention discloses a power equipment anomaly detection method and system and a medium. The method comprises the following steps: acquiring event data and a frame image; frequency domain conversion is carried out on the event data, a complex frequency spectrum of the event is obtained, and the complex frequency spectrum comprises the amplitude and phase information of the event signal on each frequency component; frame images are obtained, analysis signal conversion is carried out on the frame images, a plurality of frame images are obtained, and each pixel of the images in the plurality of frame images has amplitude and phase representation; fusing the plurality of frequency spectrums and the plurality of frame images based on adaptive attention to obtain initial multi-domain fusion features; performing time deviation correction on the initial multi-domain fusion features to obtain synchronous multi-domain fusion features; and carrying out multi-scale processing on the synchronous multi-domain fusion features to obtain a fusion image which is used for a fault identification model to carry out power equipment anomaly detection. Complete frequency spectrum information such as amplitude and phase of the event signal can be reserved in the fusion process, and the accuracy of anomaly detection can be improved.

The invention relates to the technical field of natural gas exploration and development, and discloses a CCL signal local change rate-based casing abnormity identification method and system, and the method comprises the steps: obtaining an original CCL signal of each perforation interval of an oil and gas well, coupling point data in the CCL signal, and a casing data table; the position of a casing pipe nipple is determined, coupling point labeling is carried out on an original CCL signal, and depth correction is carried out by combining labeled coupling data with a casing pipe data table; calculating a local change rate of the CCL signal after depth correction; determining an abnormal interval of the CCL signal through an abnormal threshold value of the local change rate of the CCL signal; and calculating an abnormal state score of the casing, and determining an abnormal level of the casing based on quantile threshold division. By analyzing the change of the magnetic field distribution rule implied in the CCL signal, the mathematical association between the CCL signal and the deformation state of the sleeve is established, and the utilization rate of the CCL signal in the health monitoring of the sleeve is remarkably improved.

The invention discloses a multi-protocol dynamic adaptive industrial equipment data integration system and method, and relates to the field of industrial equipment data processing, and the system comprises an identification module which is used for receiving a protocol signal sent by industrial equipment, analyzing a protocol type identifier carried in the signal, and synchronously creating a data connection channel between the equipment and the system; the generation module is used for automatically generating corresponding data interaction adaptation logic based on the signal analysis result in the identification module; according to the method, optimized interaction adaptation logic is generated by combining equipment protocol parameters and historical interaction data, reliability and real-time performance of data interaction are improved, invalid fragments are filtered during data collection, collection frequency is dynamically adjusted, data quality and equipment load balance are considered, different protocol data are processed in a standardized mode according to a unified model so as to achieve format unification, and data interaction efficiency is improved. Storage resources are dynamically allocated according to the data generation frequency and the key degree, the associated index is established, and the storage utilization rate and the data retrieval efficiency are improved.

The invention discloses a literature diffraction pattern quality evaluation and intelligent repair system and method, and belongs to the technical field of computers. The method comprises the following steps of: 1, inputting and processing data; step 2, image-text consistency alignment and consistency inspection are carried out; step 3, calculating quality parameters, constructing a quality evaluation system, and performing grading and report output on atlas data; step 4, repairing and warehousing the atlas; the system adopts a modular architecture design, is composed of an image processing module, a text analysis module, a signal analysis module, a quality evaluation module, a report generation and output module, a grading feedback module, an atlas restoration module and the like, and can flexibly adapt to different types of diffraction atlas quality evaluation tasks. According to the technical scheme, aiming at the limitation of literature image type XRD atlas quality evaluation, the evaluation system and method with intelligent repair, knowledge reasoning and self-learning capabilities are constructed, and the availability, credibility and structured processing efficiency of literature XRD atlas data are remarkably improved.

The invention relates to the technical field of motor diagnosis, and discloses a motor eccentricity fault diagnosis method and system based on current harmonic analysis, and the method comprises the steps: carrying out the power frequency component extraction of a three-phase stator current signal, obtaining a residual current signal, carrying out the complex analysis envelope transformation of the residual current signal, and obtaining an envelope analysis signal; performing synchronous interpolation resampling on the envelope analysis signal to obtain a complex envelope signal; performing frequency spectrum estimation on the complex envelope signal to obtain a discrete frequency spectrum of a module value sequence; performing equalization processing on the order spectral line amplitude in the discrete spectrum to obtain a whitened spectrum, and performing inverse transformation on the whitened spectrum to reconstruct an angular domain amplitude signal of the target motor; performing cyclostationary analysis on the angular domain amplitude signal to obtain a third-order cyclic cumulant of the angular domain amplitude signal at a specific cyclic frequency; according to the third-order cycle cumulant, the type and severity level of the eccentric fault are judged; the accuracy of motor eccentricity fault diagnosis can be improved.

The present invention relates to the field of optical fiber sensing technology, and particularly relates to an anti-interference method for drainage pipe sensing signals based on distributed optical fiber sensing. By using acquisition software to collect the original signal data of the effective monitoring section of the drainage pipe in real time, strain rate data is obtained; based on a filter, real-time integration and filtering conversion of the strain rate data are performed to obtain strain data, and then Hilbert transform is carried out to form a sequence of analytic signals; the vibration amplitude of the analytic signal is calculated continuously in short windows, the vibration amplitude distribution is statistically analyzed, and abnormally large values on the channel are obtained; based on the abnormally large values, the vibration waterfall diagram is binarized, and the Hough transform is used to detect the traffic signal part; the data in the time period of the interference signal is removed from the original data to obtain the signal in the non-interfered time period, that is, the water flow signal. This method converts the DAS signal into image data, identifies and removes the areas significantly interfered by traffic signals, so that the remaining part can better reflect the intensity of the real water flow vibration signal.

The invention provides a gear continuous spectrum vibration signal identification method based on EMD (Empirical Mode Decomposition) and HHT (Hilbert-Huang Transform), and belongs to the field of signal processing of mechanical power. The method comprises the following steps: acquiring an original vibration signal; performing FFT (Fast Fourier Transform) on the original vibration signal to obtain an amplitude spectrum; eMD decomposition is carried out on the original vibration signal to obtain a plurality of IMF components and a residual component; performing HHT (Hilbert-Huang Transform) on each IMF component to obtain an analytic signal, extracting an instantaneous amplitude and an instantaneous phase of the analytic signal, and calculating an instantaneous frequency according to the instantaneous phase; performing FFT (Fast Fourier Transform) on the instantaneous amplitude to obtain amplitude modulation information; performing FFT (Fast Fourier Transform) on the instantaneous frequency to obtain frequency modulation information; overlapping the amplitude modulation information and the frequency modulation information of each IMF component to generate a Hilbert envelope spectrum; and carrying out peak value labeling on each Hilbert envelope spectrum, and comparing the Hilbert envelope spectrums with the amplitude spectrums to finish the characteristic frequency identification of the gear vibration signals. According to the method, the problem that a traditional fast Fourier transform (FFT) method is insufficient in complex modulation signal analysis capacity is solved.

The SGPIO multi-path expansion circuit provided by the embodiment of the utility model comprises a mainboard management controller BMC and at least two complex programmable logic devices CPLDs connected with the BMC in series, each CPLD comprises an SGPIO slave node Slave and a first multiplexer MUX connected with the SGPIO slave node Slave, an output interface of the BMC is connected with the SGPIO slave node Slave in each CPLD, and an output interface of the BMC is connected with the first multiplexer MUX. Different SGPIO Slaves are configured to analyze signals of preset storage space digits in first signals transmitted by the output interface of the BMC. According to the technical scheme, the SGPIO Slave in the CPLD receives the signals transmitted by the output interface of the BMC, then the SGPIO Slave only analyzes the signals with the preset storage space bits in the first signals, and then the unanalyzed signal data is transmitted to the next CPLD to be analyzed, so that logic resources consumed by the CPLD are reduced, and the real-time performance of data transmission is improved.

The invention relates to a variable-speed bearing anomaly detection method and a readable storage medium. The method comprises the steps of collecting vibration data of a bearing as an original vibration signal; performing noise reduction processing to obtain a de-noised signal; performing Hilbert transform to obtain an analysis signal, and solving a mode of the analysis signal to obtain an envelope signal; angular domain resampling is carried out, segmentation is carried out, and Fourier transform is carried out on segmented signals to obtain an order envelope spectrum; converting the image into a grey-scale image; respectively inputting the grey-scale maps into different auto-encoders to calculate respective fusion reconstruction errors, and carrying out joint training by taking minimization of the sum of the fusion reconstruction errors of all the auto-encoders as an optimization target; the fusion reconstruction errors of all the auto-encoders are averaged to obtain an anomaly detection index, the anomaly detection index of a normal sample is used to obtain an anomaly detection threshold value, when the anomaly detection index of a new sample is larger than the threshold value, it is indicated that the bearing is abnormal, otherwise, it is indicated that the bearing is normal, and state monitoring of the variable-speed bearing is achieved.

The invention discloses a ground penetrating radar adaptive gain method comprising the following steps: S1, selecting a to-be-gained radar data profile, and obtaining the overall attenuation trend; s2, acquiring an analysis signal; s3, calculating the envelope of each column of signals and the average value of each row of the envelope matrix; s4, fitting an average value of each row of the envelope matrix according to a least square method and a model function to obtain an attenuation coefficient; and S5, obtaining a gain coefficient through the attenuation coefficient, and gaining each column in the radar data profile in the step S1 based on the gain coefficient. The method has the beneficial effects that the gain process and the calculation method have a good gain effect on original data acquired by ground penetrating radars with different frequencies under different working conditions, and parameter setting is not needed, so that the signal-to-noise ratio and the image definition of radar signals in the whole effective depth range are improved.

The invention relates to the field of railway data analysis and processing, in particular to a railway irregularity data processing method based on cEEMDAN-Hilbert. The method comprises the following steps: acquiring and preprocessing track irregularity original signals to obtain a target track irregularity signal sequence; performing CEEMDAN (adaptive noise complete ensemble empirical mode decomposition) on the target track irregularity signal sequence, and performing iterative decomposition on the signal added with Gaussian white noise to obtain a plurality of IMF (intrinsic mode function) components and a residual component; performing Hilbert transformation on each IMF component, calculating an instantaneous frequency and an instantaneous amplitude, constructing an analysis signal in a complex form, and extracting time and frequency joint distribution information; according to the instantaneous frequency and the energy result, false wavelength components are removed, signal reconstruction is carried out, and reconstructed track irregularity signals are generated. According to the invention, the identification capability of local structure disturbance can be enhanced.

The present invention discloses a method and device for laser self-mixing interference displacement reconstruction based on a single-sided time-frequency spectrum. The method comprises the following steps: S1: collecting a self-mixing interference signal from a target object, performing a time-frequency transformation based on the self-mixing interference signal to obtain a double-sided time-frequency spectrum; S2: selecting and retaining a single-sided time-frequency spectrum from the double-sided time-frequency spectrum for analysis; S3: performing an inverse transformation on the single-sided time-frequency spectrum to obtain an analytical signal; S4: extracting the real and imaginary parts of the analytical signal and dividing them to obtain a tangent signal; S5: performing an inverse tangent operation on the tangent signal to obtain a motion phase; and S6: calculating the relationship between phase and distance based on the motion phase to obtain a displacement curve of the target object. The present invention eliminates the need for additional complex optical components to obtain orthogonal signals, simplifies the composition of the measurement system, and provides a novel, efficient, and economical solution for the field of displacement measurement.

The application discloses a rolling bearing fault diagnosis method based on acoustic framing. Firstly, the collected acoustic signal of the rolling bearing is subjected to Hilbert transform to construct a double-channel analytic representation containing instantaneous amplitude and instantaneous phase information. Then, the analytic signal is divided into short-time frame segments by the acoustic framing embedding module according to a preset frame length and step, and a uniform dimension frame-level embedding vector is generated by linear mapping, so as to highlight the local transient impact feature and effectively compress the sequence length. An improved lightweight Transformer encoder is adopted for timing feature extraction, the encoder combines a low-dimensional multi-head self-attention mechanism and a feedforward network based on SiLU activation and deep convolution, so as to simultaneously model the cross-frame global dependence and the short-range local structure, and finally, the intelligent identification of the multi-class faults of the rolling bearing is completed through the global feature vector of the class token.

The invention relates to the technical field of anti-interference signal processing, in particular to an anti-interference signal processing method of an FPC (flexible printed circuit) sensing module, which comprises the following steps of: performing multilayer discrete wavelet transform decomposition on an original sensor data stream from the FPC sensing module, separating an approximation coefficient and a detail coefficient of each layer, and establishing a signal time-frequency component matrix representing signal time-frequency distribution; according to the method, multi-layer discrete wavelet transform decomposition is carried out on the original sensor data stream, signals can be analyzed in the time domain and the frequency domain at the same time, then low-frequency noise components introduced by mechanical deformation of the flexible substrate are stripped through zero setting operation on the deepest layer approximation coefficient, accidental damage to effective low-frequency components of the signals is avoided, and the accuracy and the reliability of the sensor are improved. The original form and details of the signal are reserved, and meanwhile, a surprising fraction spectrum representing the abnormal degree of data points is established, so that local sudden interference or sensor single-point failure still existing even after time-frequency domain purification can be positioned.

This invention discloses an infrasound localization method for minor pipeline leaks, comprising: acquiring the original infrasound signal of the pipeline leak; performing variational mode decomposition on the original infrasound signal to obtain IMF components; based on the IMF components, using an improved generalized cross-correlation function to obtain the time difference between the infrasound reflection to the upstream and downstream ends of the pipeline; calculating the wave velocity of the infrasound according to the pipeline characteristics; and determining the location of the pipeline leak point based on the time difference and wave velocity. The improved generalized cross-correlation function method involves: constructing an analytical signal based on the IMF components; using an improved linear adaptive weighted local approximation algorithm to obtain the local optimal solution of the IMF components; and performing inverse attenuation dispersion compensation analysis based on the generalized cross-correlation function to obtain the time difference between the infrasound reflection to the upstream and downstream ends of the pipeline. This invention can accurately locate the pipeline leak point.

This invention discloses a method and system for visual enhancement of underwater robots in turbid waters, relating to the fields of machine vision and image restoration technology. The method includes: acquiring a sequence of original color images collected in real-time by the underwater robot; mapping the pixel intensity of the original color image sequence to a complex space to construct a complex domain analytic signal; extracting the multi-directional spatial gradient tensor of the original color image, and calculating pseudo-polarization information and environmental characterization parameters accordingly; constructing a virtual phase compensation operator using the pseudo-polarization information and the environmental characterization parameters; wherein the pseudo-polarization information drives the compensation intensity of the virtual phase compensation operator, and the environmental characterization parameters are used to locally weight the compensation intensity. Its beneficial effects include: correcting edge blurring and texture obfuscation caused by wavefront phase disturbances, and significantly improving the edge steepness of structural contours in the image.

The application relates to the field of artificial intelligence, and discloses a brain-computer computing driven visual information instant analysis method and system, which comprises the following steps: acquiring real-time sensing data and neural pulse signals, analyzing a signal sensing curve to construct a brain-computer computing network, identifying visual information features through the network, analyzing a preset model computing architecture, optimizing a neuron mapping relationship to obtain an optimized mapping network, analyzing a path based on the analysis information, determining a target data time sequence distribution state, calculating an analysis efficiency value, determining an analysis optimization direction, generating an instant analysis strategy, calculating an analysis attenuation rate, determining an analysis state based on the attenuation rate, analyzing an analysis dimension, and finally constructing an optimized analysis scheme. The application can improve the intelligent analysis efficiency of visual information.

The invention discloses an electric water heater power saving method and system based on a particle swarm optimization algorithm. The method comprises the steps of data collection, adaptive mode decomposition, parameter adaptive optimization, disturbance mode division, disturbance mode denoising, threshold adaptive correction, analysis signal construction, power smooth switching and power saving strategy design. The invention belongs to the field of power saving, and particularly relates to an electric water heater power saving method and system based on a particle swarm optimization algorithm. According to the scheme, higher heat efficiency and lower energy consumption fluctuation are achieved by introducing self-adaptive VMD and chaos T distribution optimization based on the energy loss ratio; an actual heating stage, a temperature falling stage and environment heat loss are distinguished through disturbance mode division, and power disturbance caused by noise or power grid fluctuation is measured; wavelet threshold de-noising is carried out on all disturbance modes, a specific threshold function is selected, self-adaptive correction is carried out on the threshold, and then the power saving effect of the electric water heater is improved.