2266 results about "Spectrum analysis" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

A tracking and adaptive compensation method for power fluctuation of a pulse load of a ship is provided. The method includes: based on a microgrid topology model of the ship, a pulse load model of the pulse load, and an interharmonic power and pulse parameter relationship model, performing power fluctuation tracking, frequency selection and a power spectrum analysis to obtain filtering parameters of a low-pass filter and filtering parameters of a band-pass filter; performing waveform adaptation to eliminate a phase delay caused by the low-pass filter and the band-pass filter, and obtaining a control command for a power compensation strategy; and generating, by a power loop, a pulse width modulation (PWM) signal to control a switching tube in a bidirectional direct current (DC) / DC converter of each of an energy storage battery pack and a supercapacitor of a hybrid energy storage system.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention is an advanced deep learning system that combines a latent transformer core with a latent dynamics analyzer. This system processes input data into latent space vectors, which are then analyzed in parallel for both prediction and dynamic modeling. The latent transformer generates short-term predictions, while the latent dynamics analyzer derives equations of motion describing the underlying system dynamics. By integrating spectral analysis and change detection, the system can identify significant shifts in behavior, particularly useful for complex systems like financial markets. The invention enables more accurate predictions, interpretable insights, and early detection of regime changes. Its end-to-end training approach ensures all components work harmoniously, balancing predictive accuracy with physical plausibility and interpretability.

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 relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention relates to a coal conveying control system and method for a thermal power plant. The method comprises the following steps that S1, operation data of a coal conveying system are collected in real time through a preset multi-source sensor network; s2, constructing a time sequence prediction model based on an attention mechanism, and dynamically predicting the operation state of the coal conveying system; s3, executing real-time optimization calculation at the edge calculation node, and generating a control instruction set including the belt speed, the coal feeder opening degree and the transfer station cooperation strategy; s4, the control instruction is issued to an execution mechanism through a 5G-MEC network, and meanwhile key data is uploaded to a cloud end for model iteration updating; and S5, dynamically adjusting a safety protection threshold value based on an equipment vibration spectrum analysis result, and triggering a hierarchical protection mechanism when a coal blockage risk is detected. The method has the effects of effectively improving the performance of the coal conveying system and being green and energy-saving.

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 valve opening control, and discloses a valve opening control method for user-side hydraulic balance. The method comprises the following steps: carrying out parameter acquisition on the buried pipe cold storage system to obtain dynamic system parameter data; performing cold load fluctuation spectrum analysis and multi-scale decomposition based on the dynamic system parameter data to obtain a user demand prediction model and a hydraulic balance ideal flow distribution proportion; generating a comprehensive system state evaluation index according to the user demand prediction model and the hydraulic balance ideal flow distribution proportion; constructing a global optimization objective function based on the comprehensive system state evaluation index, and solving to obtain a time-phased valve adjustment strategy; and a valve flow characteristic model is constructed according to the time-phased valve adjusting strategy and the valve historical response data, the target opening degree of each adjusting valve is calculated, and a valve control execution instruction is output. The hydraulic balance strategy can be dynamically adjusted according to different working conditions, and flexible coordination of hydraulic balance and cooling requirements is achieved.

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 discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention relates to the technical field of intelligent control, in particular to a full-automatic plug-in testing machine control system and method, and the system comprises a multi-dimensional data collection module, an abnormal state recognition module, an action termination judgment module, a parameter correction module and a trajectory optimization execution module. According to the invention, based on multi-sensor array fusion, multi-physical quantity cooperative acquisition in equipment operation is realized, vibration, temperature, path and plugging force data are comprehensively calibrated by using a time axis, and through spectral analysis and three-dimensional deviation comparison, fine abnormity and trend change are dynamically revealed, and operation fluctuation and potential abnormity are identified in real time. According to the method, the reasonable interval of the insertion and extraction force is automatically calculated, the action duration and path trend are adjusted, the operation parameters are continuously corrected, equipment abnormity early warning, accurate intervention and multi-parameter linkage adjustment are achieved in the whole process, the test continuity, the execution sensitivity and the prevention and control capacity are improved, misoperation is effectively restrained, abrasion is reduced, and the high-density intelligent test requirement is met.

The invention relates to the technical field of geological disaster early warning, in particular to a geological disaster early warning method and system based on dynamic data monitoring, and the method comprises the following steps: obtaining geological displacement monitoring data, setting a time window range, respectively calculating a local extreme point and a local zero point of the monitoring data in a time window, and calculating the local extreme point and the local zero point of the monitoring data in the time window; obtaining a multi-scale reference value; and based on the multi-scale reference value, calculating a comprehensive statistical magnitude, and generating a data quality evaluation parameter. According to the method, a local extreme point and a zero point are dynamically calculated by setting a time window range, a multi-scale reference value is constructed to carry out comprehensive statistic generation and data evaluation, and collaborative discrimination of piecewise linear regression and threshold comparison are executed to form a critical index; and performing characteristic quantity recursive residual spectrum analysis and matrix decomposition operation by combining the power spectral density distribution and the spectral density matrix to establish an early warning characteristic sequence, and realizing multi-stage early warning indication output based on a grading rule.

The invention discloses a poultry health condition detection method, system and device and a storage medium, and relates to the technical field of poultry health condition detection.The poultry health condition detection method comprises the steps that poultry audio data are collected and preprocessed; based on the time-frequency characteristics of the audio data, constructing an MAE model; and performing pre-training and fine tuning on the MAE model, and evaluating the pre-trained MAE model. Based on a sound spectrum analysis technology, pre-emphasis filtering and Mel-spectrum conversion are combined, the key feature extraction capacity of poultry sound is enhanced, and the method adapts to the complex environment of a livestock farm. An MAE model is adopted, time-frequency characteristics are learned through self-supervised pre-training and a Transform structure, automatic audio acquisition and analysis and real-time health state output are realized, and the labor cost is reduced. The attention visualization technology is introduced, key frequency bands are identified in an auxiliary mode, the model interpretability is enhanced, and the whole scheme has the advantages of automation, high precision and high robustness.

The invention relates to the technical field of arc discharge control, in particular to an arc discharge machine control method based on multi-parameter collaborative optimization, which comprises the following steps of: generating a first regulation and control instruction by adopting a fuzzy PID (Proportion Integration Differentiation) algorithm based on a deviation value between a dynamic parameter feedback signal and a preset threshold value, the axial feeding speed of the servo motor, the power output curve of the arc generator and the coolant spraying time sequence are synchronously adjusted, so that the arc energy density gradient and the workpiece heat conduction rate form dynamic balance, the mechanical resonance frequency in the machining process is monitored through the vibration spectrum analysis unit, and when it is detected that the resonance intensity exceeds a safety threshold value, the coolant spraying time sequence is started. And a second regulation and control instruction is generated by triggering a frequency compensation algorithm, and the driving frequency of the servo motor is corrected in real time, so that the control precision is improved, the arc discharge process is more stable, the response speed is increased, the arc discharge time is shortened, the adaptability is enhanced, the requirements of different materials and processes can be met, the energy consumption is reduced, and the production efficiency is improved.

The invention discloses a back corona rapping electric dust remover fault early warning method based on all-condition monitoring, and relates to the technical field of fault early warning, and the method comprises the steps: obtaining a rapping signal, and extracting back corona features based on wavelet transform and spectral analysis; constructing a dynamic coupling model of rapping strength-dust layer thickness, and combining back corona characteristics to obtain working condition characteristic vectors; obtaining a working condition data set, and obtaining a health degree scoring sequence under each time window in combination with the working condition feature vectors; decomposing the health degree scoring sequence into a plurality of intrinsic mode components, identifying abnormal fluctuation features based on a Markov chain, and performing risk level judgment; through all-condition monitoring and multi-modal data fusion, a back corona feature extraction, dust layer thickness prediction and health degree scoring model is constructed, dynamic identification and risk early warning of the operation state of the electric dust remover are achieved, and the problems that a traditional method is single in monitoring dimension and lags in response are solved.

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 discloses an intelligent sealing test method and system for a wafer in a semiconductor chip, and relates to the technical field of semiconductor manufacturing, and the method comprises the steps: initializing a magneto-electric field cooperation parameter, optimizing the magneto-electric field cooperation parameter through a multi-target genetic algorithm, and driving core-shell nanoparticles to carry out the metallization filling of a high-precision through hole wafer, generating a wafer with a metalized through hole structure; based on the metalized through hole structure wafer, bonding parameters are generated through a laser Doppler frequency vibration spectrum analysis method, flip interconnection is achieved through a thermal ultrasonic virtual process, and a low-defect bonding wafer is generated; a finite element inversion algorithm is adopted, a stress distribution map of a bonding interface is established, an anti-thermal-stress packaging layer is constructed through an intelligent stress matching algorithm, and the low-defect bonding wafer is packaged; and through a multi-target genetic algorithm, magnetoelectric field cooperation parameters are optimized, core-shell nano-particles are driven to directionally deposit, and accurate control over the metallization filling process is achieved.

The invention relates to the field of water quality monitoring, and discloses a water quality on-line monitoring system based on full spectrum analysis, which comprises a full spectrum water quality analysis reference module, an equipment consistency correction module, a full spectrum water quality analysis derivative module and a quality monitoring module, wherein the reference module collects full-spectrum data through reference equipment to construct a water quality analysis reference model; the correction module uses the ResNet-C neural network to correct the spectral data difference of different devices; the derivative module combines the correction data and the reference model to realize water quality index prediction; the quality monitoring module calculates a quality evaluation coefficient through a multi-dimensional index and triggers early warning; the method is based on the Lambert-Beer theorem and the neural network architecture, solves the problems of high reagent consumption and poor equipment consistency in the traditional monitoring technology, and is suitable for real-time online monitoring of the surface water quality.

The invention discloses a vision-based molded lens edge thickness detection method and a related device. The method comprises the following steps: introducing visible gas into an edge area of a molded lens to form a gas film disturbance layer; the gas film disturbance layer is excited by illumination to generate refractive index change; collecting an interferometric phase image corresponding to the scattered light intensity; carrying out Fourier transform analysis on the interference fringes and carrying out temperature correction; and finally, calculating edge thickness distribution through feature space mapping. A gas film disturbance layer is introduced to the edge of the lens to serve as a physical signal conversion bridge, and a multi-light-path interference imaging and spectrum analysis technology is matched, so that the interference problem of a transparent material in direct optical measurement can be effectively avoided, high-precision and non-contact detection on the edge thickness of the molded lens is realized, and the detection precision is improved. And meanwhile, the device has the characteristics of high environmental interference resistance and good measurement stability.

The invention provides a cross-medium remote control communication method, device and equipment and a storage medium, which are applied to a cross-medium remote control system comprising a transmitting end and a receiving end. The method comprises the following steps that: a transmitting end responds to a key operation of a user, generates a key signal, codes the key signal, and generates a modulation electric signal containing a synchronous head signal and an instruction frequency signal; environment parameter information is collected, dynamic parameter adjustment is carried out on the modulation electric signal according to the environment parameter information, and the adjusted modulation electric signal is converted into a modulation light signal through an LED; a receiving end receives the cross-medium transmission optical signal, carries out amplification and digital processing, and extracts frequency characteristic information through spectrum analysis; and analyzing a control instruction according to the frequency characteristic information and executing corresponding equipment control operation. Through the specific time-frequency signal structure design and the multi-stage signal processing technology, the stability problem of water-air interface signal transmission is effectively solved, and low-delay and high-success-rate cross-medium remote control communication is achieved.

The invention relates to the technical field of power equipment, and discloses a transformer substation hardware fitting fault detection method, system and device, a medium and a product, and the method comprises the steps: obtaining a random characteristic energy spectrum of a vibration signal of a to-be-detected hardware fitting of a transformer substation, and determining a segmentation boundary of the random characteristic energy spectrum based on a maximum peak envelope segmentation technology; performing frequency spectrum segmentation on the random characteristic energy spectrum according to a segmentation boundary, performing frequency domain denoising on a plurality of initial frequency bands obtained by segmentation, extracting intrinsic random mode components in the denoised frequency bands, performing envelope spectrum analysis according to the intrinsic random mode components, and identifying the fault condition of the to-be-detected hardware fitting according to an analysis result. Therefore, noise suppression is effectively carried out on the vibration signals, and remarkable fault features are extracted for fault identification, so that the accuracy and reliability of hardware fitting fault signal detection are improved.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention discloses a visual defect detection method for precise and complex parts, and particularly relates to the technical field of part detection. The method comprises the following steps: dividing the surface of a precise complex part into a plurality of to-be-detected areas, calculating a gray level co-occurrence matrix characteristic value, texture characteristic distribution and a spectrum analysis result by combining image data under multi-angle and multi-light-source conditions, further comparing surface bulges and gray level difference characteristics of a normal area and the to-be-detected areas, and calculating the surface defect similarity. On the basis of surface texture complexity and defect similarity, risk misjudgment is performed through fuzzy logic analysis, a detection area is divided into high, medium and low risk areas, a detection algorithm is optimized to improve accuracy, finally, algorithm performance is verified through test data, a parameter adjustment range is limited when the misjudgment rate is increased, systematic deviation and performance reduction are avoided, and the detection accuracy is improved. Therefore, the detection stability and the overall quality control reliability are improved.

The invention discloses a quadruped inspection robot space positioning method and system based on gravity center sensing, and aims to construct a gravity center potential energy field through foot end contact force and body attitude information, form a gravity center disturbance mode in combination with fluctuation spectrum analysis, and reveal a disturbance propagation mechanism. A reverse compensation technology is adopted to generate an anti-disturbance sequence, a foot end force distribution topology is reconstructed, and active disturbance suppression is achieved; a multi-frequency detection wave beam and coherent superposition technology is introduced, an environment hologram is generated, geometric topology is reconstructed, and a high-precision environment sensing system is established; high and low impedance regions are identified through energy density analysis, and an energy optimal path is constructed in combination with double strategies of potential field bypassing and streamline tracking; establishing an oscillation analysis space for path projection simulation, identifying resonance risk points and determining stabilization parameters; finally, a coordinate reference is generated by adopting a quantum entanglement type multi-anchor-point synchronization mechanism, accurate positioning is realized by fusing environmental geometric information, and a technical closed loop from gravity center dynamics analysis to environmental perception navigation is realized.

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 digital control uninterruptible power supply parallel operation phase locking method, which relates to the technical field of digital phase locking, and comprises the following steps of: in each analysis period, acquiring an output voltage waveform signal and a current frequency disturbance control parameter of each uninterruptible power supply, and respectively executing short-time Fourier transform to obtain an output voltage waveform signal and a current frequency disturbance control parameter of each uninterruptible power supply; and converting the time domain signal into a multi-dimensional dynamic spectrum matrix, extracting frequency and amplitude parameters of a disturbance signal, and constructing a corresponding uninterruptible power supply disturbance parameter set. According to the invention, through dynamic spectrum analysis and frequency domain coupling tensor modeling, accurate extraction of UPS modulation signal features and dynamic evaluation of resonance risk indexes are realized, in combination with a risk-driven frequency intervention mechanism, frequency coupling is broken, the frequency diversity and disturbance decoupling capability of the system are improved, and the reliability of the system is improved. The stability, the anti-interference performance and the intelligent response capability of the parallel system are obviously enhanced, and early warning and active suppression of lock point resonance are realized.