858 results about "Current wave" patented technology

The invention relates to the technical field of electric power monitoring analysis, and discloses an intelligent analysis system for electric power monitoring data based on a mutual inductor. The system comprises a mutual inductor data acquisition and structuring module which acquires a current waveform, a voltage waveform and a harmonic component in real time, and generates a standardized monitoring data unit through preprocessing, field mapping and association identification establishment; the theoretical monitoring value calculation module constructs a dynamic calculation model according to historical power data, and an input standard data unit outputs a theoretical value; the rule conformity verification module is used for matching the equipment type with the operation rule and generating single compliance judgment; a multi-dimensional difference analysis module compares a theoretical value with a measured value from time domain deviation, frequency domain deviation and waveform distortion, and generates an equipment-level difference coefficient matrix; the association network construction module is used for constructing a multi-monitoring-point association map according to the equipment identifier and the position information; and the anomaly positioning and strategy generation module combines the map, compliance judgment and a difference matrix, calculates a risk score, generates an anomaly probability distribution map, positions anomaly and matches a monitoring strategy.

The invention discloses a pulsed eddy current detection method and system for a thermal power pipeline with a coating layer, and relates to the technical field of pulsed eddy current nondestructive detection.The pulsed eddy current detection method comprises the steps that pulsed eddy current detection simulation and experiment are conducted under the condition of a three-dimensional simulation model and a test piece, and excitation current waveform, amplitude and frequency parameters are determined; an induced voltage signal is collected, peak values, time points and attenuation characteristics are extracted, the corresponding relation between the wall thickness, the coating layer thickness and the welding seam structure and detection signals is constructed, a parameterized database is constructed, and temperature correction is completed; and optimizing a sensor structure and a signal acquisition scheme according to a parameterized database, obtaining detection data in a pipeline operation state, completing two-dimensional imaging of wall thickness distribution and welding seam positioning, and forming detection information covering a straight pipe, an elbow and a welding seam area. According to the method, wall thickness detection and welding seam positioning of the thermal power pipeline under the conditions of a thick wall and a thick coating layer are achieved through the full-process design of test piece modeling, simulation experiment and imaging collection.

The invention relates to the technical field of power supply system management, and discloses a centralized management method and management system for a power supply system. The method comprises the following steps: monitoring a current fluctuation sequence and voltage offset data of each node of a power supply network in a continuous operation period, and collecting an equipment state log text and load change time sequence information; identifying a time point of abnormal sudden change of current fluctuation, and extracting a characteristic parameter set of a power supply quality reduction event in combination with a voltage offset data change amplitude; matching a historical abnormal event library, positioning key operation record fragments strongly related to load change in the log, and generating a mapping relation table of equipment operation behaviors and power supply quality fluctuation through semantic analysis and time sequence alignment; and correcting the parameters of the load prediction model, outputting and executing a power supply priority dynamic adjustment strategy, and triggering automatic calibration of equipment operation parameters. According to the method, comprehensive control of the power supply system is realized, abnormity can be identified and processed in time, association between operation and quality is clear, and stability and flexibility of operation of the power supply system are improved.

The invention discloses a high aspect ratio micropore pulse abrupt change current copper filling method and system, and belongs to the technical field of printed circuit board manufacturing, and the method comprises the steps: obtaining micropore structure parameters, and generating a gradient pulse abrupt change current waveform; monitoring a deposition thickness parameter of the orifice of the micropore in real time, and when the deposition thickness parameter exceeds a preset thickness threshold value, activating the reverse peak clipping pulse group; generating a resonant composite sound field in the electroplating bath; detecting the concentration distribution of the additive in the micropores in real time, and generating a dynamic adjusting instruction according to the concentration distribution of the additive; the independent injection rate of the accelerator and the inhibitor is controlled according to the dynamic adjusting instruction; and based on the gradient pulse abrupt change current waveform, the resonant type composite sound field and the dynamic adjusting instruction, the copper filling process is executed till the target deposition parameters are achieved. According to the method, a multi-field coupling mechanism of gradient pulse dynamic peak clipping, composite sound field synergistic mass transfer and additive concentration real-time regulation and control is adopted, and defect-free high-reliability copper filling can be achieved.

The invention discloses a digital calibration system and method for dynamic response parameters of a surge protection circuit, relates to the technical field of power electronic protection and test calibration, and is used for solving the problem that an existing surge protection circuit calibration method cannot reflect device degradation and dynamic response mismatch. Surge event voltage and current waveforms are collected through a synchronous trigger mechanism, non-stationary signal features are extracted through combined time-frequency analysis, and a voltage-current phase trajectory diagram is constructed to generate a response feature matrix; performing phase-space reconstruction on the matrix, calculating Kolmogorov entropy quantification system stability, and establishing a material degradation transfer function in combination with an energy absorption sequence to generate a grain boundary degradation index; key parameters are screened through L1 regularization regression, and a quantitative relation between a grain boundary degradation index and a protection threshold value is established to realize adaptive threshold value calculation; and executing strategy switching from calibration to predictive maintenance according to the degradation trend and threshold fluctuation change, and outputting a maintenance signal to complete dynamic accurate calibration and reliable operation of the surge protection circuit.

The invention relates to the technical field of electric power intelligent judgment, in particular to an AI-based intelligent electric energy meter fault diagnosis system, which comprises an operation data acquisition module, a jump correlation judgment module, an operation deviation judgment module, a current waveform diagnosis module and an abnormity alarm module. According to the invention, through synchronous acquisition of the current data and the operation data of the refrigeration storage, whether the current jump is related to the gating action is judged, and the refrigeration performance abnormity of the refrigeration storage and the fault type of the intelligent electric meter are effectively identified by further combining multiple cross validation of baseline energy consumption and temperature change; an ideal current waveform is constructed through an AI algorithm and is compared with an actually measured waveform, so that intelligent judgment and classification of fault types are realized; meanwhile, the diagnosis result is linked with the operation and maintenance system, the response efficiency is improved, and the method is particularly suitable for scenes with high requirements on the stability of the electric meter, such as cold chain storage, and has good practical value and popularization prospect.

The invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

The invention relates to the technical field of electrical safety early warning, and discloses an electrical fire early warning method and system. The method comprises the following steps: acquiring a multi-dimensional dynamic parameter set of an electrical circuit in real time, wherein the multi-dimensional dynamic parameter set comprises a current fluctuation sequence, temperature gradient distribution data and an insulating medium loss value; performing time-frequency domain joint decomposition processing on the dynamic parameter set, and extracting current harmonic distortion characteristics, a temperature field spatial evolution mode and a dielectric loss accumulation rate; generating a current anomaly index according to a harmonic distortion characteristic and a preset threshold deviation degree, calculating a thermal runaway risk level in combination with a temperature field spatial evolution mode, and determining an insulation degradation coefficient based on a dielectric loss accumulation rate; fusing the three to construct a comprehensive fire risk index; and when the index exceeds the dynamic warning threshold value, a grading early warning signal is triggered and a hidden danger positioning map is generated. According to the method, the electrical fire early warning effect is optimized through multi-dimensional parameter acquisition and fusion analysis.

The invention discloses a grid-connected inverter control method based on second-order linear active disturbance rejection, and particularly relates to the field of grid-connected inverter control. Voltage and frequency error signals between the output side of the inverter and the power grid are obtained, a pre-synchronization control model is constructed, and voltage amplitude and frequency synchronization is achieved; a second-order linear active-disturbance-rejection controller is introduced in the frequency synchronization control process, an extended state observer is adopted to estimate the output frequency, the frequency change rate and a disturbance term, and a state error feedback control law is combined to generate a frequency regulation control quantity, so that accurate frequency control is realized; after grid connection, a dual-channel LADRC power regulation controller is constructed, and decoupling tracking control is carried out on active power and reactive power; meanwhile, on the basis of fast Fourier transform processing as a stable grid-connected state criterion, the method has the advantages of being high in control precision, high in anti-interference capability, good in current waveform quality, high in adaptability and the like, and is suitable for various grid-connected application scenes.

The invention relates to the technical field of intelligent inspection control, and discloses a multi-dimensional intelligent inspection multi-device cooperative control method. The method comprises the following steps: synchronously acquiring a real-time operation parameter set of a plurality of monitored devices, wherein the real-time operation parameter set comprises a vibration spectrum, a temperature distribution map and a current waveform; performing multi-source heterogeneous data fusion processing on the real-time operation parameter set to generate a multi-dimensional feature vector; based on a preset collaborative optimization target, performing dynamic association analysis on the multi-dimensional feature vector to generate an equipment operation feature vector; and performing a matching decision according to the equipment operation feature vector and a preset cooperative constraint condition, and outputting a cooperative control instruction set. According to the method, through multi-dimensional data acquisition, fusion processing and dynamic association analysis, comprehensive perception and cooperative control of the multi-device operation state are realized, the coordination and reliability of device group operation are improved, and the method is suitable for multi-device cooperative operation and maintenance in a complex scene.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the technical field of energy storage equipment monitoring, and discloses an energy storage equipment operation state real-time early warning and supervision method based on an LSTM hybrid model. The method comprises the following steps: firstly, collecting current fluctuation time sequence data, a voltage deviation distribution curve and a temperature gradient evolution graph of the energy storage equipment, and generating a multi-source operation parameter set; constructing a long-short-term memory network dominant capacity attenuation prediction model based on the current fluctuation time sequence data, and constructing a time sequence convolution assisted voltage anomaly identification model based on the voltage deviation distribution curve; then monitoring the capacity attenuation trajectory and internal impedance fluctuation data of the equipment in real time, and dynamically activating the target model to generate a risk early warning instruction; and finally, inputting an early warning instruction into the regulation and control unit, and adjusting a charging and discharging strategy or starting a protection mechanism in real time. According to the method, through multi-source parameter fusion and dynamic model activation, comprehensive and accurate monitoring and quick response of the operation state of the energy storage equipment are realized, and the safety and stability of equipment operation are improved.

The invention relates to the technical field of electric power engineering automation, in particular to a low-voltage equipment power factor automatic adjusting system and method based on end compensation, and the system comprises a load monitoring module, a compensation strategy generation module, an end compensation execution module, a dynamic balance correction module and an effect evaluation module. According to the invention, the sequential relationship between the load fluctuation and the compensation demand can be accurately identified by continuously monitoring the current waveform, the voltage waveform and the phase difference change trend of the low-voltage equipment, and the compensation strategy is dynamically formulated in combination with the response duration and the compensation demand matching rule, so that the compensation response agility and the matching property of the fluctuating load are improved; dynamic scheduling is carried out according to the capacitor switching capacity and the combination relation in compensation execution, switching weight configuration is analyzed and optimized through the power factor change rate before and after compensation and the load synchronization characteristic, the dynamic balance of the compensation process is enhanced, the power factor stability and the load fluctuation matching capacity after compensation are effectively improved, and the compensation efficiency is improved. And the adaptability and continuity of the adjusting effect are enhanced.

The invention discloses an electric power utilization anomaly detection and early warning method based on the Internet of Things, which relates to the technical field of electric power utilization and comprises the following steps of: performing fitting comparison on identified inductive current fluctuation and a behavior residual error template generated based on a real load behavior; analyzing a response time window, a fluctuation amplitude track and a behavior continuity characteristic, and judging whether the inductive current fluctuation generated by a non-target loop belongs to a real load change behavior or not under the condition that a plurality of loops are grounded together according to the response time window, the fluctuation amplitude track and the behavior continuity characteristic; and combining the behavior residual error fitting score, the phase consistency value and the frequency response stability value to generate a signal credibility judgment sequence, and carrying out credibility grade division on the inductive current fluctuation. According to the method, the problem of false fluctuation misjudgment caused by inductive signal interference under the condition that multiple loops share the grounding condition is solved, accurate identification and dynamic regulation and control on the authenticity of inductive current fluctuation are achieved, and therefore the accuracy and reliability of power utilization anomaly detection and early warning are improved.

The invention discloses an insulation resistance and capacitance measuring circuit and method based on monopulse double-edge analysis, and relates to the related field of the electrical insulation online monitoring technology, and the circuit comprises a detected object and an insulation detection circuit. The insulation detection circuit comprises a controlled pulse voltage source, a coupling resistance network, a current sensor, a switch control module, a signal modulation circuit and a processor. The measurement method implemented based on the circuit comprises the following steps: injecting a common-mode voltage pulse into a transmission line of the power transmission system by adopting a pulse transmitter; pulse current response is collected in real time, a current oscillogram is drawn, and a three-point sampling method is introduced to record a current value for insulation impedance parameter calculation; according to a load condition, a double-edge verification method is applied based on a pulse current attenuation characteristic, and insulation impedance parameters including a resistive component and a capacitive component are independently calculated by analyzing a pulse current rising edge and a pulse current falling edge. According to the invention, non-intrusive synchronous monitoring of resistive and capacitive components of line-to-ground impedance is realized, and the detection precision is improved.

The invention discloses a variable frequency driving device energy recovery control system based on direct power supply of a high-voltage power grid, and relates to the technical field of electrical transmission and energy recovery control. Updating the power grid impedance model in real time by using a complex impedance calculation method; load operation state parameters are dynamically adjusted in combination with motor end output power, rotating speed change and torque calculation; according to the method, regenerative energy is accurately calculated, a feedback path is adjusted according to voltage fluctuation and frequency deviation of a power grid, a feedback current fluctuation ratio and a power factor are monitored, a feedback path conduction proportion is optimized in real time, when the current is unstable or the power factor is abnormal, a suboptimal path is automatically switched, and continuous and stable operation of a system is guaranteed; according to the invention, the energy feedback efficiency and the power grid compatibility of the high-voltage power grid variable-frequency driving device are effectively improved, and the method is suitable for energy feedback application of a high-power motor driving system.

The invention belongs to the technical field of mutual inductor verification, and particularly relates to a data processing method for testing a current mutual inductor, so as to solve the technical problem that an existing verification method cannot truly reflect the comprehensive error of the mutual inductor, and the method comprises the following steps: S1, obtaining the current waveform data of a tested current mutual inductor; s2, deducting the exponential decay aperiodic component from the original current waveform data to obtain corrected current waveform data; s3, reconstructing to obtain de-noised current waveform data; s4, acquiring fundamental waves of the primary side current and the secondary side current and setting amplitude and phase angles of subharmonics; s5, calculating an initial ratio error and an initial angle error of the current transformer; s6, generating a ratio error compensation vector and an angular difference compensation vector; and S7, respectively carrying out vector summation on the initial ratio error and the initial angle error with the ratio error compensation vector and the angle error compensation vector to obtain the final ratio error and the final angle error of the current transformer. And the test accuracy and reliability of the ratio difference and the angle difference of the current transformer are improved.

The invention relates to the technical field of fire early warning, and particularly discloses a fire-fighting electrical fire early warning method and system based on the Internet of Things, and the method comprises the steps: configuring an edge computing gateway, and collecting a current waveform signal and a high-frequency electromagnetic radiation signal of an electrical loop based on the edge computing gateway; extracting harmonic characteristics and electromagnetic radiation characteristics; constructing a risk value prediction and propagation model based on the graph neural network model according to the power grid topological structure, generating a data packet when the coupling relationship between the harmonic characteristics and the electromagnetic radiation characteristics meets a joint triggering condition, inputting the data packet into the risk value prediction and propagation model, and transmitting the data packet to the power grid. A dynamic risk value and a fault source position are output based on a risk value prediction and propagation model, through bimodal acquisition, electrical fire hazards can be found in an early stage, real-time feature extraction is performed by using an edge computing gateway, the data transmission amount is reduced, modeling is performed on power grid topology through a graph neural network model, a fault propagation path can be accurately predicted, and the fault propagation efficiency is improved. And the early warning precision is improved.

The invention discloses a wide temperature range compensation method for resistive current of a lightning arrester, which belongs to the technical field of online monitoring of power equipment, and comprises the following steps: acquiring waveform data under a reference low-temperature working condition, and establishing an initial capacitive harmonic base; acquiring working condition data at any temperature in real time, performing harmonic decomposition, and analyzing to generate a nonlinear distortion feature vector; dynamically correcting the initial base by using the feature vector, and carrying out subtraction operation on the total current harmonic wave to obtain a final resistive current harmonic wave vector; and reconstructing an instantaneous resistive current waveform through inverse transformation and calculating a characteristic value. According to the technical scheme, the initial capacitive harmonic wave base is established, the nonlinear distortion feature vector is generated based on the harmonic distortion of the real-time working condition quantification temperature and valve plate nonlinear coupling, and dynamic feedback correction is conducted on the base, so that the real resistive current waveform in the wide temperature range can be accurately separated and reconstructed, and the accuracy of the real resistive current waveform in the wide temperature range is improved. And the accuracy and reliability of lightning arrester state evaluation are improved.

The invention relates to the technical field of radio frequency power matching, and discloses a direct-current radio frequency hybrid high-power network matching optimization method. The method comprises the following steps: collecting current fluctuation data of a direct-current power supply module and power reflection data of a radio-frequency emission module, and generating a hybrid network impedance characteristic set; calling a power grid topology analysis engine to analyze the set to obtain an impedance track characteristic matrix; performing dynamic trajectory optimization on the matrix based on a preset frequency domain stability constraint rule to generate corrected impedance trajectory distribution; fusing the corrected track distribution and the radio frequency load dynamic response data, and constructing multi-band matching topological feature representation; performing parameter decoupling on the topological feature representation through a nonlinear matching mapping engine to generate an optimal matching parameter configuration table; and finally adjusting the voltage stabilization coefficient of the DC power supply module and the tuning capacitance value of the radio frequency emission module according to the configuration table. According to the method, efficient matching optimization of the hybrid network is realized, and the stability and adaptability of system operation are improved.

The invention discloses an AC / DC power distribution network power quality control method and device, and the method comprises the steps: collecting power quality data in an AC / DC power distribution network, carrying out the feature analysis of the power quality data, and obtaining a current feature parameter and a voltage abnormality identifier; carrying out differential compression processing on the power quality data based on the current characteristic parameters and the voltage abnormity identification; transmitting the compressed data and feature information through a redundant channel comprising a main communication link and a standby communication link; and generating electric energy quality alarm information according to the transmitted data, and performing power grid operation decision control according to the information. According to the method, the electric energy quality data of the plurality of Internet of Things electric energy quality sensors in the AC / DC power distribution network are collected, and harmonic analysis is performed on the current waveform of the electric energy quality data, so that the accuracy and robustness of feature extraction are ensured; therefore, the requirements of the modern AC / DC hybrid power distribution network for high-precision, high-reliability and quick-response power quality control can be met.

The invention relates to the technical field of power distribution network fault early warning, and discloses an early warning system and method for a power distribution network ground fault. The data acquisition module of the system acquires multi-mode monitoring data such as zero-sequence current waveform, grounding wire current amplitude phase, insulator leakage current and environment temperature and humidity of the power distribution network in real time, and comprehensively reflects the operation state of the power distribution network. And the feature fusion module performs spatial-temporal feature compensation on the multi-modal monitoring data to generate a fault feature tensor, so that the insulation degradation evolution period of the topological node of the power distribution network is determined, and the insulation state change is accurately evaluated. And the risk deduction module analyzes a dynamic communication path of the topological structure of the power distribution network, performs fault conduction modeling, determines a ground fault risk situation level in combination with multi-modal monitoring data, and comprehensively considers fault conduction characteristics and operation conditions. The early warning decision module generates a dynamic early warning strategy according to the insulation degradation evolution period and the risk situation level, and the early warning mode is flexibly adjusted.

The invention provides a tunable laser source output wavelength adjusting device and method, and the method comprises the steps: starting a tunable laser source, carrying out the preheating, and resetting a motor motion unit; acquiring an initial light-emitting wavelength based on a pre-stored relation curve between the light-emitting wavelength of the laser source and the position of the motor; controlling the output wavelength of the laser source through the coarse tuning parameter of the motor motion unit, traversing the tunable output range of the tunable laser source, and collecting the wavelength position of the reflection peak value of the fiber bragg grating and the corresponding optical power value; according to the reflection peak wavelength position, through voltage regulation and control of stacked piezoelectric ceramics, step scanning is carried out according to a target wavelength, and a laser source output wavelength is dynamically adjusted to obtain a fine scanning wavelength; and judging whether the deviation between the fine scanning wavelength and the target wavelength meets the preset precision or not, if so, locking the output wavelength, otherwise, repeatedly regulating and controlling until the conditions are met. According to the invention, traditional current-wavelength dependence is broken through, and wavelength demodulation is realized through electromechanical collaborative calibration and two-stage scanning.

The invention provides a motor multi-band vibration suppression method and a motor multi-band vibration suppression device based on electromagnetic-mechanical cooperative regulation and control. According to the method, vibration signals are collected in real time, and spectral analysis is carried out to identify vibration frequency distribution; dividing the vibration signal into a low-frequency band, a middle-frequency band and a high-frequency band according to a spectral analysis result, and respectively corresponding to a mechanical vibration source, an electromagnetic excitation source and a structural noise source; a control strategy is dynamically selected based on a vibration frequency band, a low-frequency band triggers a mechanical regulation and control module to output a reverse force to perform vibration suppression, a middle-frequency band triggers an electromagnetic control module to optimize a current waveform and a PWM modulation mode, and a high-frequency band starts a cooperative control strategy of the electromagnetic and mechanical control module; through real-time communication between the controller and each module, vibration feedback signals are continuously monitored, and control parameters are dynamically adjusted according to working condition changes so as to maintain the vibration suppression effect. According to the invention, through cooperative adjustment of electromagnetic control and a mechanical structure, active suppression of vibration of the motor in multiple frequency bands is realized.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

The invention discloses an electric energy quality data monitoring method, and relates to the technical field of electric power system monitoring and analysis, and the method comprises the following steps: S1, obtaining an original voltage and current waveform set; s2, performing time-frequency domain decomposition by using the waveform set; s3, performing feature fusion by using the feature component set; s4, performing time serialization by using the electric energy quality index set; and S5, performing multi-dimensional structured integration by using the time sequence index data. By setting a voltage and current characteristic component coupling mechanism, unified expression of voltage and current characteristics can be ensured on an original signal level, and fine-grained description of electric energy quality characteristics is realized in an index calculation link, so that the problems of data dimension separation and insufficient index precision in the prior art are effectively solved. The method not only improves the comprehensiveness and reliability of the electric energy quality monitoring result, but also lays a solid data foundation for subsequent time serialization and structuralization integration.

The invention relates to the technical field of wireless communication, in particular to an OTA upgrading method and system for Bluetooth sound equipment. The method comprises the following steps: acquiring to-be-upgraded firmware data and currently played audio data, and acquiring power amplifier current feedback data of a sound speaker by sending a double-frequency detection signal containing 21kHz and 22kHz; acquiring equipment transmission capability confirmation data according to a silent response mode of the sound equipment to the preset handshake signal; and calculating a nonlinear distortion coefficient of the loudspeaker according to the intensity of the 1kHz difference frequency component in the power amplifier current feedback data, and obtaining real-time channel evaluation data based on current waveform deviation analysis. According to the method, the nonlinear distortion degree of the loudspeaker is accurately evaluated through the 1kHz difference frequency component generated by the 21kHz and 22kHz dual-frequency detection signals, so that the reliability of data transmission is more accurately predicted.

The invention discloses a motor winding fault monitoring method and system, and relates to the technical field of winding fault monitoring. According to the motor winding fault monitoring method, three-phase current waveform signal data and running state data of a set motor are obtained; based on a pre-trained current waveform identification model, in combination with three-phase current waveform signal data of the set motor, a winding fault induction characteristic value of the set motor is analyzed; based on the running state data of the set motor, the winding comprehensive disturbance risk characteristic value of the set motor is analyzed, and the winding fault risk assessment characteristic value of the set motor is analyzed in combination with the winding fault induction characteristic value. Therefore, the dynamic change rule of the current waveform in the time sequence is effectively described, the identification depth and the time response speed of fault causes are improved, and the accuracy of fault risk assessment is enhanced.

The invention discloses an automobile fastener production energy consumption optimization control method and system, and relates to the technical field of fastener production, and the method comprises the following steps: obtaining a real-time current waveform signal and a power grid fundamental wave voltage signal of an inversion unit in an energy feedback link, building a phase dynamic drift model through a time difference analysis algorithm, and carrying out the calculation of the phase dynamic drift model; continuous mapping of energy flow direction change is formed on a time axis; and based on the established phase dynamic drift model, extracting an offset rate continuous change section between the current waveform and the power grid fundamental wave, constructing a phase trend prediction function by using a sliding window algorithm, and converting the time characteristic of the energy flow direction change into a phase prediction signal trajectory. Through phase dynamic drift modeling and lead compensation control, synchronization of feedback current and power grid voltage is realized, and ripple current and element stress are reduced; and through energy density change rate extraction and dynamic filtering regulation and control, the energy flow is stable and continuous, and efficient and energy-saving control over fastener production is achieved.

The invention relates to the technical field of electronic measurement, in particular to a multifunctional comprehensive test method and system for a direct-current power supply, and the method comprises the steps: initializing a test; a static performance test step; a dynamic performance test step: driving a direct-current electronic load module to execute a time sequence instruction sequence in the dynamic load test file, and acquiring an output voltage and an output current waveform through a high-speed data acquisition module in the execution process; a ripple noise test step: switching the high-speed data acquisition module to an alternating current coupling mode and acquiring a high-frequency noise waveform in a constant load current section in the time sequence instruction sequence; a correlation analysis step; and a test termination step: controlling the load to return to zero and closing the tested DC power supply. Through automatic configuration and test execution of the system, the complexity of manual operation is reduced, and the test efficiency is improved. And especially in a high-frequency test scene, the test time is remarkably saved through automatic execution.