1429 results about "Spectrum analysis" patented technology

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention provides a multi-mode ultrasonic fusion pressure vessel weld defect nondestructive testing method and system, and relates to the technical field of nondestructive testing. According to the method, geometric parameters of a welding seam are obtained through three-dimensional laser scanning, and an optimal scanning parameter set is generated; driving ultrasonic phased array equipment to scan for one time and synchronously acquire shear wave full-matrix capture and longitudinal wave linear scanning data; performing energy flow angular spectrum analysis and envelope analysis on the bimodal data, extracting defect feature parameters and constructing a three-dimensional feature tensor; carrying out multi-dimensional feature fusion by adopting Tucker decomposition, and enhancing a core tensor through physical modeling; generating three types of defect indication diagrams including a defect existence possibility diagram, a defect relative scale diagram and a defect space orientation diagram from the enhanced feature tensor; and the three types of indication diagrams are visually presented for comprehensive interpretation of detection personnel. Through multi-modal data fusion and physical modeling enhancement, the defect identification accuracy and detection efficiency are remarkably improved, the false alarm rate is reduced, and reliable technical support is provided for pressure vessel welding seam safety detection.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The invention relates to the technical field of motor detection, and discloses a motor instantaneous torque measurement method and system based on a high-bandwidth sensor, and the method comprises the steps: synchronously collecting a torque signal and a multi-source interference variable through the high-bandwidth sensor, and forming an original torque sequence after time synchronization superposition; extracting disturbance frequency characteristics through multi-scale wavelet transform and spectral analysis; a pre-trained adaptive filter model is utilized to generate a filter coefficient matched with the working condition, and primary filtering is completed; residual fluctuation is suppressed in combination with Kalman state estimation, and an optimized torque estimation value is obtained; parameter self-adaptive compensation is realized through online calibration weight updating, and a stable torque measurement sequence is output; load abrupt change pre-judgment and threshold value dynamic updating are achieved based on time-frequency correlation analysis, and finally rapid measurement convergence after working condition abrupt change is ensured through a closed-loop reset mechanism. According to the method, the problem of low instantaneous torque measurement precision in the prior art can be solved.

The invention provides a laser positioning method for improving gemstone cutting precision, and belongs to the technical field of gemstone cutting laser positioning. A gemstone crystal refractive index tensor model and a ray tracing algorithm are established to simulate a propagation path of light in a gemstone; a thermo-optic effect prediction model is adopted to predict a focus drift distance according to temperature distribution and laser power data and calculate wavefront predistortion phase distribution for compensation, and a zero-vibration input shaping filter is designed for a galvanometer scanning system to suppress residual vibration. A composite control strategy that feedforward control compensates inertia lag and feedback control corrects position errors is adopted to drive a galvanometer, the inherent frequency of a system is recognized through spectral analysis, and the scanning frequency is adjusted to avoid resonance. The technical problem that in the long-time machining process, due to the thermo-optic effect and vibration of a galvanometer scanning system, the laser focus point position generates accumulated drifting, and the cutting precision is reduced is solved.

The invention discloses a transcranial magnetic navigation and curative effect evaluation system based on electroencephalogram power spectrum analysis, particularly relates to the technical field of electroencephalogram signal processing, and is used for solving the technical problem that existing transcranial magnetic stimulation treatment lacks interference evaluation and stimulation path optimization on a cortical collaborative area. According to the method, electroencephalogram signals of a testee in a resting state and a task state are collected, phase-amplitude coupling indexes between frequency bands are extracted, a frequency-crossing coupling matrix is constructed, a nested chain structure with modulation direction consistency is identified, a stable resonance channel group is extracted from a channel intersection set to form a collaborative active region, and the collaborative active region is constructed. And after mapping to a standard brain map, carrying out space matching with a TMS target spot, evaluating an interference risk level, formulating a stimulation avoidance strategy, recording side reactions during treatment, and constructing a curative effect scoring window in a normalization manner, so as to realize synchronous optimization of stimulation planning and curative effect evaluation.

The invention provides a power distribution network single-high-frequency resonance rapid tracking and adaptive suppression method, a multi-mode dynamic cooperative adaptive suppression system is established based on a PCCVF adaptive method, and the damping characteristics of a power distribution network are remodeled by injecting compensation current into a power distribution network system so as to realize broadband resonance suppression of the power distribution network. The compensation current injection method comprises the following steps: step 1, detecting resonant frequency deviation of a power distribution network through real-time FFT (Fast Fourier Transform); 2, performing dynamic phase angle correction based on a phase prediction residual error of real-time frequency deviation; step 3, constructing a layered impedance remodeling module, and ensuring stable power transmission; 4, establishing a frequency-variable impedance model of the power distribution network based on resonance energy spectral density analysis, dynamically optimizing parameters through fuzzy logic of a bell-shaped membership function, and feeding harmonic compensation current into the power distribution network by using a space vector pulse width modulation technology; according to the invention, single high-frequency resonance can be effectively suppressed, the response and tracking performance of the system is improved, and the spectrum analysis efficiency and bandwidth occupation are optimized.

The invention discloses a cross-working-condition fault diagnosis method based on knowledge embedding and multi-scale attention, belongs to the technical field of cross-working-condition fault diagnosis, designs a domain knowledge embedding signal processing method based on wavelet packet transformation, envelope spectrum analysis and statistical feature analysis, and constructs a knowledge feature matrix and a statistical feature matrix. Fault information which is not easily influenced by working condition changes is highlighted, and the dependence of a deep learning model on a target domain sample is effectively reduced; a multi-scale attention mechanism is designed to extract depth fault features in an input matrix, and compared with an original multi-head attention mechanism, multi-scale is introduced, so that the parameter quantity of a model is reduced, and the feature extraction capability is more flexible; by embedding domain knowledge highlighting domain invariant fault information into a signal processing end, the method can show excellent variable working condition fault diagnosis performance when a target domain sample is completely lacked.

The invention discloses an intelligent water quality monitoring and pollution source identification method based on full spectrum analysis, and relates to the technical field of environmental monitoring optical sensing, and the method comprises the following steps: carrying out dynamic background stripping on a water body pollution metadata set to obtain a dynamic background spectrum, and building a spectrum purification model to carry out characteristic purification on the dynamic background spectrum to obtain a water body pollution data set; generating a pollution characteristic spectrum; performing multi-dimensional feature aggregation and hydraulic drive topology reconstruction on the pollution feature spectrum to generate a watershed pollution dynamic propagation relationship network diagram; performing physically constrained space-time convolution and propagation inversion on the watershed pollution dynamic propagation relationship network diagram to generate a pollution source probability distribution thermodynamic diagram; and carrying out responsibility association mapping on space anchor point coordinates of the pollution characteristic spectrum and pollution source center positioning of the pollution source probability distribution thermodynamic diagram, and generating a visual traceability report. Through dynamic background stripping and feature purification, high-fidelity extraction of pollution features under a complex optical background is realized, and the detectability of trace pollutants is remarkably improved.

The invention discloses a steel rolling process-based bar temperature filtering control method, which relates to the technical field of steel rolling production and comprises the following steps of: acquiring bar temperature original data, corresponding steel rolling process parameters and steel grade component data in advance; and a material temperature feature library is constructed, steel grade components are identified in real time according to an online spectrum analysis technology, and a matched filtering parameter reference template is called. By constructing the material temperature feature library and combining the online spectrum analysis technology, accurate recognition of different steel types and intelligent calling of the matched filtering parameter template are achieved, the problem that a traditional filtering method cannot adapt to the characteristic difference of the steel types is effectively solved, the universality and pertinence of filtering control are remarkably improved, and the method is suitable for large-scale popularization and application. Effective smoothness of high-fluctuation temperature data is guaranteed, the problem of lag or excessive filtering caused by a fixed window is avoided, and the dynamic adaptability of the system to a complex steel rolling scene is greatly enhanced.

The invention provides a power load prediction method based on feature decoupling and time-space diagram modeling, and particularly relates to the technical field of power load prediction, and the method comprises the steps: preprocessing the historical data of a power load, obtaining a normalized multivariable load sequence, and decomposing the normalized multivariable load sequence into a trend term and a season term through a learnable convolution kernel decomposition algorithm; a dominant period is extracted from seasonal term spectrum analysis, and multi-scale down-sampling is carried out according to the dominant period to generate a multi-scale sequence. Based on a multi-scale sequence, an adaptive mixed hop message aggregation mechanism is adopted, cross-scale time dependence and inter-variable high-order association are dynamically fused, and aggregation features are obtained. And performing combined prediction on the trend term and the aggregation feature, and outputting a load prediction result after inverse normalization. According to the method, the technical problem that an existing graph neural network model is difficult to accurately model a time-space dependency relationship of dynamic change under multiple scales in power load prediction is solved, so that the power load prediction precision and the model generalization ability are remarkably improved.

The invention discloses a wind turbine generator blade damage judgment method and detection system based on voiceprint detection, and belongs to the technical field of wind turbine generator blade detection. The damage judgment method comprises the following steps: acquiring environmental parameters of a wind power plant, and establishing a sound velocity correction model based on the environmental parameters; acquiring a blade resonance sound signal, and correcting the blade resonance sound signal based on the sound velocity correction model to obtain a blade resonance sound signal with a high signal-to-noise ratio; performing FFT spectrum analysis on the blade resonance sound signal with the high signal-to-noise ratio, identifying the peak position of the first three-order natural resonance frequency of the blade, and performing analysis to obtain the current modal frequency, damping ratio and sub-band energy distribution entropy value of the blade; historical baselines of the modal frequency, the damping ratio and the sub-band energy distribution entropy of the blade in the healthy state are obtained, and the modal frequency offset, the damping ratio change rate and the acoustic impedance spectrum entropy of the blade are obtained; and wind turbine generator blade damage determination is carried out by using a high-precision time difference detection technology.

The invention relates to the technical field of equipment health management and fault diagnosis, and particularly discloses a time-frequency enhanced current diagnosis method. According to the method, multi-phase current, temperature and rotating speed data are synchronously obtained, and a standardized sequence is generated through time alignment and noise suppression; self-adaptive spectrum analysis is carried out based on the working condition robust index, and a multi-scale energy diagram and frequency domain features are generated; fusion embedding is constructed through time domain feature extraction and time-frequency alignment mapping; and carrying out dynamic weight fusion and quality judgment in combination with the cross-modal consistency score, and finally generating a lightweight deployment model. According to the method, the anti-interference performance and interpretability of current diagnosis characteristics under multiple working conditions are effectively improved, accurate extraction and lightweight deployment of fault characteristics are realized, and the adaptability and reliability of a diagnosis system in a complex industrial environment are remarkably enhanced.

The invention discloses a milling process vibration state monitoring method and system for a machine tool milling cutter, and belongs to the technical field of intelligent monitoring. The method comprises the following steps: acquiring a vibration signal of a milling cutter under a reference processing condition, and establishing a reference template containing normal vibration characteristics; dynamically adjusting an alarm threshold value, and starting fault diagnosis analysis; performing envelope spectrum analysis on the vibration signal triggering diagnosis; preliminarily judging the affiliation fault type of the abnormal vibration through a preset fault classification model; and carrying out association verification by combining the attribution fault type and the spatial position information when the abnormality occurs, confirming the root cause of the fault, and outputting a targeted classification alarm instruction. According to the invention, the cutter handle single sensor is linked with phase slicing and dynamic threshold and coordinate verification, so that the faults of the cutter, the cutter rest and the workpiece are accurately distinguished and directionally alarmed, and the problems that a fault source cannot be distinguished by a single channel and the false alarm rate is high in the prior art are solved.

The invention discloses a water sample pollution traceability analysis method fusing absorption-three-dimensional fluorescence spectrum characteristics, and relates to the field of water sample spectrum analysis traceability. According to the method, high-precision prediction of pollutant indexes is realized by utilizing multi-dimensional information of a three-dimensional fluorescence spectrum and an absorption spectrum of a water sample, qualitative and quantitative traceability of main pollution components and potential emission sources in a water body is carried out based on correlation analysis of the prediction indexes, a pollutant feature library and an emission source database, and the method has the advantages of high accuracy and high accuracy. The integration of water quality monitoring and pollution source management is realized, and a systematic and real-time technical means is provided for pollution analysis of a complex water body.

The invention relates to the technical field of power transmission line safety monitoring, and particularly discloses a self-adaptive calibration method for an ultrasonic wind speed sensor, which comprises the following steps of: arranging an orthogonal ultrasonic probe array at the top of a tower, and compensating probe offset caused by swinging of a tower body in real time through GNSS / IMU (Global Navigation Satellite System / Inertial Measurement Unit) fusion positioning; integrating a temperature, humidity and pressure sensor and vibration spectrum analysis to construct an environmental parameter compensation model; double-frequency ultrasonic waves are adopted to detect the ice accumulation thickness of the protective cover and correct the sound path distance; deploying a lightweight LSTM model at an edge computing unit to realize minute-level dynamic parameter adjustment; electromagnetic interference is suppressed by combining linear frequency modulation spread spectrum and beam forming technologies; and establishing a three-height-layer wind speed gradient model to generate a galloping early warning. The problem of measurement distortion caused by tower deformation, icing and parameter solidification of a traditional sensor is solved, all-working-condition operation from-40 DEG C to 60 DEG C is supported, and the monitoring reliability in a strong electromagnetic environment is remarkably improved.

The invention relates to the technical field of motor state monitoring, in particular to a motor state monitoring method and system based on data driving, and the method comprises the following steps: synchronously collecting a motor voltage signal amplitude time domain sample and a motor current signal amplitude time domain sample for a power frequency period in a motor operation state; and calculating an instantaneous phase difference between each corresponding sample point to obtain an instantaneous phase difference original data stream of the motor. The method comprises the following steps: synchronously acquiring voltage and current amplitudes during operation of a motor in real time, and calculating an instantaneous phase difference between the two amplitudes to obtain an electromagnetic state original data stream; further executing high-pass filtering and performing spectral analysis to filter interference signals and low-frequency trend influences, and improving the recognition precision and robustness of instantaneous phase difference jitter features; meanwhile, voltage pulses are actively injected into the winding, echo signals are collected, and rapid capturing and positioning of the abnormal state of the winding are achieved through propagation time delay calculation and impedance characteristic construction.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention relates to the technical field of power equipment fault diagnosis, and particularly discloses a pole-mounted circuit breaker mechanical fault diagnosis method and system, and the method comprises the steps: firstly, synchronously collecting a dynamic force-displacement signal, a high-frequency acoustic emission signal and a broadband vibration signal in the switching-on and switching-off process of a circuit breaker operating mechanism; then, the acoustic emission and vibration signals are decoupled into an impact source component and a friction source component which are statistically independent through a blind source separation algorithm; performing envelope spectrum analysis on the impact source component to extract impact characteristics, and performing energy calculation on the friction source component to obtain friction noise energy characteristics; finally, on the basis of dynamic force-displacement curve fitting analysis, in combination with impact characteristics and friction noise energy characteristics, distinguishing and delimiting of mechanical wear and lubrication degradation faults are achieved. According to the invention, the problem that the existing diagnosis technology cannot effectively identify and distinguish the coupling fault of mechanical wear and lubrication deterioration is solved, and early warning and accurate diagnosis of the mechanical fault of the pole-mounted circuit breaker are realized.

The invention discloses a mixed gas absorption spectrum analysis method and system based on variational mode decomposition, specific laser is injected into an optical resonant cavity unit, and a detector unit continuously monitors the light intensity change and records a light intensity attenuation signal; pre-processing the recorded light intensity attenuation signal; carrying out VMD decomposition on the preprocessed ring-down signal to obtain a plurality of IMF components, respectively introducing CO2 and CO gases with known concentrations into the optical resonance unit, and recording spectral data of each single gas component; calculating the similarity and contribution degree of each IMF component, and setting a weight combination to form a joint score; the gas with the highest joint score is selected, the score is compared with an adaptive threshold value, and when the score is higher than the adaptive threshold value, the IMF component is marked as the characteristic component of the corresponding gas; and finally, gathering and outputting the marked IMF components according to gas types to obtain characteristic signals of the gases, thereby realizing multi-component gas separation in the mixed gas absorption spectrum.

The invention discloses a distribution network power line anti-interference communication method based on adaptive filtering, and belongs to the technical field of distribution network power line communication. According to the method, a communication signal adopting a frequency shift keying modulation mode is acquired from a power line coupler of a distribution network through a signal acquisition module, the communication signal is preprocessed by using a digital signal processing unit, spectral analysis is performed on the preprocessed signal, and characteristic parameters of interference noise are extracted; a coefficient vector of a finite impulse response adaptive filter is initialized based on the characteristic parameters, the order of the filter is in direct proportion to the reciprocal of the dominant interference frequency, fine adjustment is carried out based on noise energy distribution through a table look-up method or a linear mapping relation, and the weight coefficient of the filter is adjusted in real time in the communication process. The adjustment step length is dynamically adjusted according to the real-time estimated signal-to-noise ratio, and finally, the processed signal is output through the adaptive filter and data demodulation is carried out. The method is mainly used for improving the anti-interference capability and the communication reliability of the power private line communication of the distribution network.

The invention discloses a cutter state real-time monitoring and compensating method, system and equipment and a medium, and relates to numerical control precision machining. In the method, a cutting edge optical image, an acoustic emission signal time domain waveform, a vibration acceleration parameter and real-time temperature data of a cutting area of a cutter in the machining process are collected; measuring the maximum width and depth of a wear belt through an image to form a wear characteristic quantized value, and judging a wear over-limit state; judging the damage state based on the continuous ultralimit time of the instantaneous mutation rate of the acoustic emission signal; judging the abnormal vibration grade and state through vibration spectrum analysis; and inputting the vibration spectrum and the temperature data into a prediction model to obtain a position offset prediction quantity. And calculating a size deviation value according to a wear / damage state, calculating a coordinate offset according to vibration abnormity, generating a cutter geometric compensation instruction or a motion trail compensation instruction, and completing real-time compensation operation. According to the scheme, the fine abnormal change of the cutter in the machining process can be captured in time, and compensation is conducted according to the fine abnormal change.

The invention provides an electric energy quality on-line monitoring device and a cloud platform cooperation system, and relates to the technical field of electric energy quality management, and the device comprises a signal collection unit which obtains three-phase voltage and current signals in parallel through a Rogowski coil and a capacitive voltage divider; the event detection unit is used for synchronously executing harmonic spectrum analysis and transient event capture by adopting a parallel processing architecture realized by an FPGA (Field Programmable Gate Array); the edge processing unit is connected with the event detection unit, and is configured to encode the transient event features into a Gramer angle field image and compress steady state data into feature vectors; the multimode communication unit comprises a 4G / 5G wireless module and an optical fiber Ethernet interface; and the strategy execution interface outputs a treatment instruction to the compensation device through the D / A converter and the current loop. Through collaborative innovation of the device and the cloud platform, the power quality monitoring and treatment efficiency is remarkably improved.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention relates to the technical field of beam fusion, and discloses an irregular water area underwater topographic surveying and mapping method based on multi-beam fusion, which comprises the following steps: acquiring multi-voyage sounding data of a target irregular water area by using a multi-platform multi-beam sounding system such as a coastal patrol ship and an unmanned surveying ship, and performing sound velocity profile correction, attitude correction and tide level reduction to obtain a target irregular water area; unifying to the same space-time reference and plane rectangular coordinate system, and constructing an initial underwater terrain digital elevation model in an irregular water area range by adopting Gaussian weighted interpolation; defining an influence area by taking a hydraulic structure as a center, decomposing a local terrain into a background terrain and a scouring residual error, performing polar coordinate conversion and angular terrain spectrum analysis in the influence area, and calculating an axisymmetric form dominance index of the scouring pit; and constructing a topographic form shaping factor based on the axisymmetric form dominance index of the scouring pit, the maximum scouring depth and the influence radius, performing frequency domain modulation on the Fourier coefficient, and reconstructing the corrected local topography to obtain a final underwater topography DEM.

The invention discloses a frequency modulation control method and system for a thermal power generating unit, and relates to the technical field of frequency modulation control, and the method comprises the steps: collecting a frequency modulation instruction signal of a power grid, decomposing the frequency modulation instruction signal, and obtaining a plurality of intrinsic mode function components and residual components; performing spectral analysis on each intrinsic mode function component, marking the component of which the main frequency exceeds a high-frequency disturbance frequency threshold as a high-frequency disturbance component, and calculating a fluctuation amplitude and a change rate; a thermal system control instruction is generated through a PID control algorithm, the coal feeding amount and the main steam pressure are adjusted, and an auxiliary compensation control instruction is generated for the high-frequency disturbance component through a fuzzy control algorithm; a thermal system control instruction and an auxiliary compensation control instruction are converted into operation parameters of a thermal power generating unit control mechanism and issued for execution, when the control effect deviation continuously exceeds a deviation threshold value, an optimization process is triggered, and the frequency modulation stability and the equipment safety of a unit in the environment that new energy is accessed to a power grid in a high proportion are remarkably improved.

The invention belongs to the technical field of gas detection, and relates to a ship exhaust gas multi-component synchronous online monitoring system and method based on a laser Raman spectrum. The system comprises a sampling module, a preprocessing module, an analysis module, a data processing module, a blowback module and a flow measurement module. The method comprises the following steps: starting and self-checking the system; sample collection, pretreatment and flow velocity measurement; laser excitation and signal acquisition; carrying out spectrum analysis and concentration inversion; data output and system maintenance; and performing periodic automatic calibration. According to the invention, a complete monitoring process chain from sampling, pretreatment, flow measurement, analysis to back flushing is creatively integrated. The high-efficiency pretreatment and back flushing design greatly reduces the influence of high-dust and high-humidity environment on the analysis system, and ensures the long-term stability and reliability of the system.

The invention discloses a multi-scale performance evaluation and optimization method and device for well cementation cement stone, and relates to the technical field of oil exploitation, the well cementation cement stone is subjected to nanoindentation test and scanning electron microscope microstructure test, and a mechanical property-microstructure test result is obtained; performing electronic computer tomography and mercury injection test on the well cementation cement stone, and constructing a pore network three-dimensional model; performing triaxial mechanical property test, thermogravimetric analysis and thermal expansion test on the well cementation cement stone to obtain a thermal-mechanical coupling test result; performing X-ray diffraction and X-ray fluorescence spectrum analysis on the well cementation cement stone to obtain mineral phase information and element information of the cement stone; according to the method, a mechanical property-microstructure test result, topological parameters, a thermal-mechanical coupling test result, cement stone mineral phase information and element information are fused, an optimal design scheme for target well conditions is generated, multi-dimensional performance testing and characterization are achieved, and an accurate basis is provided for well cementation cement stone optimal design and quality control.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention relates to the technical field of anti-counterfeiting mark manufacturing and detection, in particular to a white spirit bottle cap anti-counterfeiting mark manufacturing method based on laser miniature carving and a detection device.The white spirit bottle cap anti-counterfeiting mark manufacturing method comprises the steps that S1, on the basis of the micro-nano optical principle, micro-nano structure parameters containing periodic grooves and protrusions are generated through algorithm optimization; s2, according to the micro-nano structure parameters, an engraving path file matched with ultra-short pulse laser equipment is generated; when the white spirit bottle cap anti-counterfeiting mark is used, through the micro-nano structure design and multi-angle dynamic information display, the anti-counterfeiting mark has extremely high complexity and uniqueness, the counterfeiting difficulty and the counterfeiting cost are greatly increased, the anti-counterfeiting performance of the white spirit bottle cap can be improved, the dynamic change characteristics of the anti-counterfeiting mark are captured through multi-angle imaging, and the anti-counterfeiting mark has good anti-counterfeiting effect. The micro-nano spectrum analysis module detects the authenticity of the micro-nano structure from the microscopic level, and the micro-nano spectrum analysis module and the micro-nano structure complement each other, so that the accuracy and reliability of detection can be remarkably improved, and the possibility of misjudgment and missed judgment can be reduced.