36 results about "Wavelet noise" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

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

The invention provides a gas ultrasonic transducer fast matching method, device and equipment and a storage medium, original measurement data such as flight time are obtained by deploying an ultrasonic transducer in a gas ultrasonic flowmeter in a gas conveying pipeline, and multi-dimensional data acquisition is carried out in combination with other sensor parameters. Wavelet noise reduction and dynamic time warping processing are carried out on collected signals, signal quality and time sequence consistency are improved, time domain, frequency domain, environment and statistical features are extracted, and multi-dimensional feature vectors are formed. Modeling is carried out through a time sequence feature branch and an environment feature branch, weighted fusion is carried out by adopting an attention mechanism, and the recognition capability of the model on key features is enhanced. A machine learning model is trained based on fusion features, a multi-objective loss function and a data enhancement strategy are adopted, the prediction precision and generalization ability of the model are improved, and a compensation value is output to calibrate the original traffic in real time. According to the method, the accuracy and stability of gas flow measurement in a complex environment are effectively improved, and the method has a good engineering application prospect.

The invention relates to the technical field of industrial data processing, in particular to a model-driven FlinkSQL (Structured Query Language) dynamic optimization method, which comprises the following steps of: generating candidate SQL (Structured Query Language); carrying out parallel acquisition and noise reduction processing; constructing a causal dependency graph and embedding the graph; constructing a spatio-temporal feature vector and performing steady-state judgment; performing disturbance sandbox verification and stability judgment; a fault-tolerant strategy is injected, and DML is compiled and produced; and deploying tasks and monitoring and updating in real time. Physical signals of industrial equipment are stripped through a wavelet noise reduction technology, then operation indexes of the Flink platform are subjected to structured expression in combination with causal dependency graph embedding, quantitative classification is performed on current operation and ideal steady state, robustness is verified through multiple cycles, adaptive parallelism and a fusing strategy are injected into SQL, and the method has the advantages of being high in robustness and high in reliability. And the reference base point is automatically updated according to the monitoring result, so that the problems of low SQL execution efficiency, insufficient stability and difficulty in adapting to complex industrial scenes caused by neglecting environment change and system load fluctuation during operation are effectively solved.

The invention discloses an intelligent carbon anode quality detection system, particularly relates to the field of electrolytic aluminum, and comprises a data acquisition module, a data preprocessing module, a data analysis module and a comprehensive judgment module. According to the method, a full-process data acquisition system covering raw materials, forming, roasting and appearance is constructed, accurate acquisition of multi-source data is realized by using equipment such as an X-ray fluorescence spectrometer and a hydraulic sensor, the problem of dispersion of traditional detection data is solved, and the detection accuracy of the multi-source data is improved through multi-stage pretreatment methods such as outlier elimination, sliding window filtering, temperature compensation and wavelet noise reduction. Dimension difference and sensor noise interference are effectively eliminated, and data reliability is improved. A multi-level evaluation model composed of a raw material comprehensive index, forming energy density, roasting maturity and appearance integrity is innovatively established, and intelligent comprehensive evaluation and accurate grading management and control of the carbon anode quality are achieved in combination with a dynamic weighting comprehensive quality index formula and a four-level grading mechanism.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention discloses a rainfall monitoring method and device based on millimeter wave radar, and relates to the technical field of meteorological monitoring. The method comprises the following steps: controlling a 24 / 60GHz commercial millimeter wave radar to transmit a millimeter wave signal of an adaptive parameter to a preset area; a raindrop echo signal is received, and clutters are removed through Kalman filtering and wavelet noise reduction; extracting a one-dimensional energy feature, a two-dimensional speed feature and a speed-energy two-dimensional distribution matrix; inputting a low-error model trained by a random forest algorithm, calculating a rainfall energy value and mapping the rainfall energy value into a rainfall level; data is output through a LoRa / ZIGBEE network; the device comprises a millimeter wave radar module, a signal processing module, a calculation output module and a wireless communication module. The problems of single monitoring dimension, high cost and difficult deployment in the prior art are solved, non-contact, low-cost, high-real-time and distributed rainfall monitoring is realized, the error is less than or equal to 5%, the response period is less than or equal to 100ms, and the method is suitable for traffic, municipal administration, emergency and other scenes.

The invention relates to a multi-fault real-time early warning system for a power distribution cabinet, and relates to the technical field of power distribution cabinet detection. Partial discharge, mechanical vibration, insulation dielectric loss and environmental parameters are synchronously acquired through a distributed sensor array; performing electromagnetic interference filtering and wavelet noise reduction preprocessing on the original data, and extracting the characteristics of pulse frequency, vibration kurtosis, dielectric loss change rate and the like; a'rule base + random forest 'mixed model is adopted to identify fault types and confidence; fault positioning with the precision of + / -3cm is realized based on sensor delay inequality and a triangulation positioning algorithm, and fault components are determined in combination with a component topology mapping table; early warning levels are divided according to fault types, confidence coefficients and component importance, and customized reports are dynamically pushed; a standard signal is injected every 24 hours to generate a compensation coefficient, and the drift error of the sensor is corrected in real time.

The invention relates to the field of distributed optical fiber sensing signal noise reduction, in particular to a distributed optical fiber sensing signal noise reduction method based on reinforcement learning enhancement. The method comprises the following steps: S1, generating a distributed optical fiber sensing signal original image matrix; s2, setting a two-dimensional wavelet noise reduction hyper-parameter set; s3, generating an action set; s4, based on a Markov decision process, performing enhanced model training on the two-dimensional wavelet noise reduction hyper-parameter setting of the original image matrix of the distributed optical fiber sensing signal; and S5, carrying out noise reduction on the original image matrix of the distributed optical fiber sensing signal. According to the method, the spatial correlation between image pixel points can be effectively utilized, reinforcement learning updating is automatically carried out on the wavelet noise reduction hyper-parameters, and the noise adaptability of the algorithm is improved. In addition, in the algorithm training process, a reference-free reward function is established, a known noise-free clean signal does not need to be used as a reference, and the algorithm is more suitable for being used in actual engineering.

The invention discloses a new energy station cable terminal partial discharge signal identification method and system, and the method comprises the following steps: collecting an original signal, carrying out the preprocessing of the original signal, and carrying out the wavelet threshold noise reduction processing, and obtaining a noise reduction signal; performing sliding slicing processing on the denoised one-dimensional long time sequence signal, and inputting the processed signal into the constructed partial discharge identification model to train the model; and inputting the target signal into the trained partial discharge identification model to obtain a time position and a continuous range of a real partial discharge event in the long time sequence signal, thereby realizing effective identification and separation of the partial discharge signal and corona interference of the cable terminal of the new energy field station. Wavelet noise reduction, a time window and one-dimensional convolutional network depth feature recognition are integrally designed, partial discharge signals can be stably recognized in the strong noise and mixed pulse scene of the new energy station cable terminal, and a reliable data basis is provided for insulation state evaluation and operation and maintenance early warning of the new energy station cable terminal.

The invention relates to a long-term monitoring and early warning method suitable for a large-span steel frame beam, and the method specifically comprises the steps: laying a distributed fiber grating sensor network at a key stress part of a target steel frame beam, and synchronously collecting an original deformation signal in real time; a temperature compensation algorithm and a wavelet noise reduction algorithm are used for preprocessing the collected various original deformation signals; identifying the deformation state of the key stress part through an improved conjugate beam method on the basis of the multiple original deformation signals after preprocessing and the structural model information of the target steel frame beam; constructing a steel frame beam deformation prediction model, and predicting the future deformation trend of the target steel frame beam by using the preprocessed historical and real-time signal time sequence data; and a steel frame beam abnormal deformation diagnosis model based on the graph neural network is constructed, abnormal recognition is performed on the deformation state of the key stress part based on a preset threshold value, and early warning is performed on the abnormal part in combination with the future deformation trend of the target steel frame beam.

The invention discloses a distributed optical fiber temperature-sensitive fire detection algorithm based on CNN-LSTM and an attention mechanism, and aims to solve the problems of high false alarm rate, response lag and poor scene adaptability in the traditional detection technology. According to the system, temperature data are collected through a distributed optical fiber sensing unit at the interval of 2 seconds and the spatial resolution of 0.5 m, after Z-Score standardization, db4 wavelet noise reduction and 12-dimensional feature enhancement preprocessing, the temperature data are input into a CNN-LSTM hybrid model to extract spatial and temporal features, key areas and moments are focused in combination with an attention mechanism, and a fire risk index of 0-100 is output. Model training adopts transfer learning and a Focal Loss function to realize cross-scene rapid adaptation and sample imbalance processing, and parameters are updated through incremental learning every 30 days. Practice shows that compared with a traditional threshold value method, the method has the advantages that the false alarm rate is reduced by more than 80%, smoldering fire early warning is advanced by 25 seconds, and the method can be widely applied to fire safety monitoring of complex scenes such as tunnels, transformer substations and comprehensive pipe galleries.

The invention provides an insulation state detection method, system, equipment and medium, and relates to the field of electronic device detection.The method comprises the steps that a discharge signal of a power module is obtained, high-pass filtering and wavelet noise reduction are conducted on the discharge signal, and a discharge pulse is obtained; recording the morphology change of the bubbles at the three bonding points of the substrate, the electrode and the silica gel of the power module; analyzing the discharge pulse based on a phase resolution partial discharge map to obtain partial discharge parameter changes, and classifying the partial discharge parameter changes to obtain insulation defect types; and performing evaluation according to the morphology change of the bubbles and the insulation defect type to obtain an insulation degradation state. On the basis of collaborative analysis of bubble evolution and discharge characteristics, the insulation state of the high-voltage power module is monitored, in-situ observation of bubble evolution of a glue-solid interface is realized, discharge signal characteristics are extracted, and a mapping relation of bubble form-discharge mode-signal parameters is established.

The invention provides a terahertz time-domain spectral signal noise reduction method and system for transformer aged insulating oil, and the method comprises the steps: collecting a terahertz time-domain spectral signal of an aged insulating oil sample, and carrying out the parameter optimization of variational mode decomposition through a whale optimization algorithm, according to the method, an optimized variational mode decomposition algorithm is adopted to decompose terahertz time-domain spectral signals of aged insulating oil into a series of intrinsic mode function components, then independent component analysis is adopted to separate the intrinsic mode function components into noise and effective signals, and finally the effective signals are screened out by calculating information entropy of independent components. And reconstructing the terahertz time-domain spectral signal of the aged insulating oil after noise reduction. The technical problems that the wavelet basis function and the decomposition layer number of wavelet noise reduction are difficult to determine, the noise reduction effect is restricted by the endpoint effect and mode aliasing problem of empirical mode decomposition, and the noise reduction effect of variational mode decomposition is poor are solved.

The present application relates to the technical field of equipment parameter optimization, and discloses a method and system for optimizing the parameters of a liquid cooling system for a data center. The method includes: performing wavelet noise reduction processing on the equipment heat load data to construct a time-series heat generation model; fusing the thermodynamic model with the time-series heat generation model to generate a comprehensive heat model, setting the objective function and constraints, and outputting a parameter combination; the parameter optimization model generates a standard parameter combination based on the parameter combination, substitutes the standard parameter combination into the parameter optimization model for evaluation, and outputs the optimal parameter combination; adjusts the operating parameters of the liquid cooling system in real time, and collects the actual system operating data of the liquid cooling system, compares the actual system operating data with the expected equipment heat load data of the data center, obtains the parameter deviation, and dynamically adjusts the operating parameters based on the parameter deviation. The present application improves the efficiency and accuracy of the liquid cooling system parameter optimization.

The embodiment of the application provides a cascade anti-interference circuit suitable for an electrocardio monitoring device, which comprises a sliding mean filter module, a notch filter module, a lifting wavelet decomposition module, a threshold calculation module, a threshold processing module and a lifting wavelet reconstruction module; while ensuring a small circuit scale, the ECG signal collected by the wearable electrocardio monitoring device is subjected to hierarchical noise reduction processing, and high signal-to-noise ratio ECG signal output is realized; for the ECG signal collected by the wearable electrocardio monitoring device, first, sliding mean filtering is performed to filter out baseline drift noise caused by human respiratory movement and other activities; the power frequency interference caused by the wired and wireless connection of the electromagnetic environment around the device is suppressed by using the notch filter module; finally, wavelet noise reduction is realized by using the lifting wavelet decomposition and reconstruction and threshold noise reduction method, the electromyographic interference caused by the autonomous or unconscious movement of the wearer is suppressed, and high signal-to-noise ratio ECG signal is output, which is used for physiological parameter extraction and electrocardio diagnosis.

The invention discloses a wireless remote control and data return device of a coal bunker cleaning machine, relates to the technical field of wireless remote control of coal bunker cleaning machines, aims to solve the technical problem that the type and residual quantity of coal accumulated in a traditional bunker cannot be accurately recognized, and comprises a field sensing module, a data processing module, a wireless transmission module and a remote control module. In a noise reduction unit of a data processing module, a wavelet noise reduction algorithm is used for processing image data acquired by an AI visual camera. Mist noise generated by dust interference can be effectively filtered out, key features such as edges and textures of coal deposits can be clearly reserved, the problem that viscous coal and wet powdery coal as well as blocky coal and viscous coal are difficult to distinguish manually is solved, the type difference and distribution condition of the coal deposits in the bin can be clearly captured, a high-quality image basis is provided for subsequent accurate recognition of the types of the coal deposits, and the coal deposits in the bin can be accurately recognized. And the coal deposit characteristics are judged no longer depending on subjective experience of operators. The problem that the type and residual quantity of accumulated coal in a bin cannot be accurately recognized is solved.

The invention discloses a distributed ocean photovoltaic panel automatic anti-bird device and method. The method comprises the following steps: S1, networking and sensing acquisition; s2, identification and threshold adjustment; s3, linkage and expelling are carried out; s4, performing feedback and data recording; s5, performing optimization and operation and maintenance calibration; the problem of adaptation of special marine environments is solved in a targeted mode, environmental interference such as high salt fog and light intensity fluctuation is effectively resisted by deploying salt fog-resistant weather-resistant edge nodes, integrating a multi-module ad hoc network and adopting wavelet noise reduction processing, stable operation of equipment and data acquisition purity are guaranteed, and the blank that a conventional bird prevention scheme is insufficient in marine environment adaptability is filled up; the method overcomes the pain point of inaccurate bird recognition and intention judgment, dynamically adjusts a threshold value by means of deep learning, a behavior model and track time sequence analysis in combination with environmental parameters, accurately distinguishes passing birds from resident birds, accurately judges landing and excretion intentions, greatly reduces false triggering and missed triggering probabilities, and improves the reliability of recognition decision.

A multi-sensor fusion intelligent diagnosis method under a multi-protocol communication architecture comprises the steps that an architecture containing a hardware layer and a protocol conversion layer is constructed, the hardware layer accesses heterogeneous sensor data through a module supporting protocols such as RS485 and CAN, the protocol conversion layer unifies the data into a standardized format, and after wavelet noise reduction and normalization preprocessing are conducted, the data are sent to a server; a self-adaptive weighted fusion algorithm is used for fusing data, extracting time domain, frequency domain and trend characteristics, inputting a model combining a BP neural network and a D-S evidence theory to realize diagnosis, dynamically adjusting early warning threshold push information, solving the problem of data islands, and improving the fault identification accuracy.

The application relates to the field of intelligent monitoring, and discloses a device defect detection method based on infrared identification, which comprises the steps of infrared thermal imaging data acquisition, noise reduction processing, nonlinear heat conduction modeling, high-order partial differential optimization and variational regularization inversion solving. An infrared thermal imager collects device surface temperature distribution data, after multi-scale wavelet noise reduction and boundary heat flow balance optimization, a nonlinear heat conduction model is established by using temperature-related thermal conductivity coefficients, the heat diffusion boundary is optimized in combination with a high-order Laplace operator, and the defect heat source distribution is inverted through a variational regularization method, so that the geometric characteristics, thermal parameters and position coordinates of the defects are accurately extracted. The application can adapt to complex working conditions, realize high-precision detection and characteristic analysis of device defects, overcome the problems of noise interference, insufficient boundary processing and weak defect inversion capability in the prior art, and is widely applicable to fault diagnosis and health monitoring of industrial equipment.

The invention discloses a distributed spectrum sensing noise stripping method based on wavelet gradient, and belongs to the technical field of communication and artificial intelligence crossing. The method comprises the following steps: acquiring original spectrum data through a distributed cognitive radio node; performing sliding window segmentation, Hanning window weighting and power spectrum density calculation on the data; the preprocessed data are input into a differentiable wavelet noise separation layer, the layer serves as a learnable one-dimensional convolution kernel through a parameterized biorthogonal wavelet filter, and end-to-end trainable multi-resolution analysis is achieved in a deep learning framework; calculating the power spectrum density gradient of the high-frequency detail coefficient in real time, and quantifying the noise abrupt change intensity based on a central difference method; dynamically generating a gating weight, and selectively suppressing the wavelet coefficient of the high-gradient frequency point; inputting the processed data into a one-dimensional convolutional neural network with residual connection for classification; and results are fused by adopting a clustering type collaborative decision-making mechanism.

The present application relates to the field of near-field wavelet processing imaging technology, and particularly discloses a kind of multi-source air gun excited passive source near-field wavelet processing imaging method and device, the method comprises: through near-field detector, near-field wavelet data is collected, and near-field wavelet data is matched with navigation positioning data, the loading of observation system is completed. Again using the difference of each channel type of active source and passive source, passive source data is extracted. Again according to the development characteristics of near-field wavelet noise, key noise such as bubble direct wave is suppressed, the signal-to-noise ratio of data is improved and seabed shallow layer image is obtained, so as to fill the blank of domestic near-field wavelet imaging field, promote the development of near-field wavelet imaging processing technology, and lay a foundation for the standardization and standardization of multi-source near-field wavelet data processing flow.

This invention relates to the field of bridge inspection technology, specifically to a bridge displacement detection method and system based on phase information. The method includes: acquiring acceleration vibration response signals from at least two measuring points on the bridge; filtering out noise and drift using a bandpass filter; performing a Hilbert transform on the filtered signals to obtain an analytical signal and extracting the instantaneous phase sequence; calculating the cross-correlation function of the instantaneous phase sequences of adjacent measuring points and determining the phase difference time sequence through the maximum value; correlating the phase difference time sequence with the distance between measuring points to convert it into a relative displacement time sequence; integrating the relative displacement time sequence twice to obtain a cumulative relative displacement value; using this cumulative value as a benchmark, and combining it with the absolute displacement calculation results of each measuring point to infer the overall bridge displacement; and performing wavelet noise reduction on the overall displacement to obtain the final detection result. This method can improve the accuracy and stability of displacement detection and is suitable for displacement monitoring of bridge structures.

The invention discloses a welding penetration control method and system based on LabView, and relates to the technical field of welding process control. The method comprises the steps that a welding voltage signal in the welding process is collected; a LabView processor is used for conducting data format conversion, noise reduction processing and frequency domain analysis on the welding voltage signals, and the oscillation frequency of a molten pool is extracted; the oscillation frequency of the molten pool is compared with the critical penetration frequency in an expert database, and the penetration state of the current welding process is judged; and the welding system and / or the motion control system are / is controlled to execute corresponding control actions according to the judgment result, so that welding heat input is adjusted, and closed-loop control over the penetration state is achieved. The noise reduction treatment adopts a self-adaptive multi-scale correlation weighted generalized threshold wavelet noise reduction method, and the oscillation frequency of the molten pool is obtained through peak group proportion analysis. According to the method, online recognition and real-time regulation and control of the welding penetration state can be achieved, and the method has the advantages of being high in anti-interference capacity, stable in feature extraction and high in control precision.

The invention discloses a photovoltaic power generation power prediction method and system based on entropy weight wavelet denoising, and the method comprises the steps: obtaining photovoltaic power generation power and meteorological historical data, and forming an original time series data set; entropy weight adaptive threshold wavelet denoising processing is carried out on each feature sequence, the threshold is adaptively adjusted by calculating wavelet coefficient energy entropy of each scale, and noise suppression and signal retention are realized by adopting an improved threshold function shrinkage coefficient; inputting the de-noised data into a hierarchical adaptive multi-branch activation bidirectional gating cycle unit network for training to obtain a prediction model; and finally, performing power prediction on the real-time noise reduction data by using the prediction model. According to the invention, the precision and robustness of photovoltaic power prediction are effectively improved.

The invention relates to a lithium battery remaining service life prediction method and system and a medium. The method comprises the steps of data preprocessing, indirect health factor IHI extraction, fusion of the extracted indirect health factors IHI, and wavelet noise filtering of the fused indirect health factors IHI. Network optimization: dividing particle swarms in a particle swarm optimization (PSO), independently optimizing each sub-swarm, introducing sub-swarm merging and splitting to form a dynamic multi-swarm particle swarm optimization, coupling a forgetting gate and an input gate in an LSTM network structure, and inputting hyper-parameters needing to be manually determined in an improved CNN-LSTM network into the dynamic multi-swarm particle swarm optimization. Performing round optimization to finally determine hyper-parameters in the network; and predicting the remaining service life of the lithium battery. According to the method, more accurate parameter calculation is realized, and the problems of local optimum and the like are avoided.

The invention provides an intelligent identification system and method for micro defects of a cladding layer on the inner wall of a large metal pipeline based on an ultrasonic phased array, and relates to the technical field of energy storage power stations. In order to solve the problems that a cladding layer (with the thickness of 0.5-1.0 mm) on the inner wall of an injection-production pipe of a compressed air energy storage power station easily falls off to form micro defects (with the diameter smaller than or equal to 1 mm) after long-term service, traditional ultrasonic detection is greatly interfered by roughness, and the signal discrimination degree is poor, an ultrasonic phased array probe is used for scanning and collecting signals along the outer wall of a pipeline, and after wavelet noise reduction and Fourier time-frequency conversion, the cladding layer (with the thickness of 0.5-1.0 mm) on the inner wall of the injection-production pipe of the compressed air energy storage power station is obtained. And extracting a voiceprint feature vector by using a convolutional neural network, identifying the defect size in combination with a dynamic matching algorithm, and outputting a defect image through full-focus imaging. According to the method, the advantages of high precision of the ultrasonic phased array and AI intelligent identification are integrated, the micro cladding layer falling defect can be rapidly and accurately identified, the anti-interference capability is high, and a reliable detection means is provided for safe operation of an injection-production pipe.

The invention provides a method for realizing rapid evaluation of surface uranium content by using unmanned aerial vehicle aviation release measurement, and relates to the technical field of unmanned aerial vehicle aviation radioactivity measurement. The method for realizing rapid evaluation of the uranium content of the earth surface specifically comprises the following steps: S1, system background calibration; s2, correcting atmospheric radon; s3, calibrating air sensitivity; s4, uranium content inversion; s5, carrying out data noise reduction processing; and S6, three-dimensional mapping. According to the technology, cosmic rays and equipment background are synchronously deducted through water area measurement, the radon correction coefficient is dynamically adjusted in combination with morning and evening baseline flight, the problems that aerial work of an unmanned aerial vehicle aerial radioactivity measurement system is difficult and radon correction is not accurate are solved, a height attenuation coefficient three-dimensional correction model and a wavelet noise reduction algorithm are adopted, small crystal noise interference is overcome, and the radon correction accuracy is improved. Through uranium content grading drawing and geologic model fusion, the method achieves the quick positioning of an abnormal region, is small in inversion error, is high in verification goodness of fit, remarkably improves the uranium mine verification efficiency and safety of a plateau / unmanned region, and reduces the production cost.

The invention discloses a while-drilling ultrasonic device and method for detecting inherent frequency of hole bottom rock. The while-drilling ultrasonic device comprises a drill bit, a detection ultrasonic unit, a power ultrasonic unit, external control equipment and the like. The detection ultrasonic unit is arranged near a drill bit in a short section mode and comprises a multi-array-element transducer and an echo information processing and entropy weight-Topsis dynamic weighting algorithm module, db4 wavelet noise reduction and FFT transformation are sequentially carried out on echo signals through circumferential sector-by-sector, radial grading and axial layering three-dimensional frequency sweeping logic so as to extract formants, and the detection ultrasonic unit is used for detecting the ultrasonic signals. Calculating a weighted average frequency through a dynamic weighting algorithm; the power ultrasonic unit receives the frequency instruction and adjusts vibration parameters in real time. According to the device and the method, the hole bottom full-space inherent frequency is accurately detected, the importance difference of rock blocks on the rock breaking efficiency is dynamically distinguished, the frequency matching degree and the energy consumption efficiency of ultrasonic rock breaking are improved, the adaptability is high, and original factory installation and later transformation of various frequency-adjustable ultrasonic rock breaking equipment are supported.

The invention relates to the field of power battery data processing methods, in particular to a multi-working-condition sensor method and system suitable for a power battery, and the method comprises the steps: synchronously reading the voltage, current and temperature data of the power battery, and setting the sampling frequency and range; calculating a temperature gradient, calculating an equivalent multiplying power, and determining a working condition type; performing composite drift compensation by applying a temperature gradient and an aging drift term, executing wavelet noise reduction, and updating filter parameters; calculating the median of each sensor group and the standard deviation of all the sensors, combining the confidence components of the three parts, and calculating the confidence of each sensor through normalization processing; the weight of each sensor is calculated, a weighted average value is calculated, a second derivative is estimated, dynamic smooth constraint is applied to execute adaptive weighted fusion, and anomaly detection and isolation are carried out; issuing high-precision data, generating a health report, and triggering an early warning mechanism; checking calibration conditions, executing zero point / gain calibration, and updating compensation parameters. According to the invention, the abnormity of the power battery can be dynamically and accurately found in real time.