4448 results about "High Frequency Waves" patented technology

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention relates to the field of motor health management and predictive maintenance, in particular to a motor stator winding insulation state evaluation method and system based on a digital model. Comprising the following steps: S1, collecting high frequency of a motor stator winding, and generating multi-physical-quantity real-time data; s2, calculating a dynamic capacitance reference value according to real-time data of multiple physical quantities; s3, performing subtraction operation on the high-frequency equivalent capacitance measurement value and the dynamic capacitance reference value, and extracting an insulation degradation residual signal; s4, constructing a self-adaptive dynamic detection threshold according to multi-physical-quantity real-time data; s5, judging whether the absolute value of the insulation degradation residual signal is greater than a self-adaptive dynamic detection threshold or not: if so, judging that an insulation degradation event occurs; if not, judging that the operation state is a normal operation state; and S6, in response to the insulation degradation event, updating the insulation degradation index, and generating insulation state evaluation based on the updated insulation degradation index. According to the invention, false alarm under severe load fluctuation is avoided, and the accuracy and reliability of evaluation are significantly improved.

The invention discloses an intelligent fire detection method based on artificial intelligence video analysis, and particularly relates to the technical field of computer vision. The method comprises the following steps: acquiring a video monitoring stream, extracting spatial color features and morphological features of an image, constructing a multi-scale time sequence feature graph group, performing joint modeling on color disturbance and structural disturbance by using a dual-channel attention neural network, and outputting a suspected fire area probability graph; candidate fire source areas are screened in combination with boundary disturbance consistency analysis and red channel high-frequency fluctuation detection; the texture stability, the disturbance directivity and the historical smoke evolution characteristics are integrated through a multi-modal fusion judgment model, the fire state is finally judged, an alarm result is output, meanwhile, the system can mark the fire position and the development trend path, and visual tracking is achieved. The method has the advantages of high robustness, high sensitivity and low false alarm rate, and is suitable for intelligent early warning of early fire in a complex environment.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

The invention provides a magnesium alloy electric drive main shell mold machining precision control method and system, and belongs to the field of precision numerical control machining. The method comprises the steps that dynamic milling force, key point temperature and position signals of all shafts are synchronously collected through a multi-source sensor; a recursive least square method is adopted to identify the dynamic rigidity and damping coefficient of the tool-workpiece system on line, and the thermal deformation drift distance is calculated in combination with a temperature signal; predicting a three-dimensional deformation error vector in real time based on the dynamic model, thermal deformation and geometric errors, and constructing a space compensation field; macro motion compensation is achieved by modifying numerical control program coordinate points, and meanwhile high-frequency micro-amplitude compensation is conducted through a spindle tail end fast tool servo system. And establishing an adaptive disturbance observer to perform online estimation on unmodeled disturbance and feed-forward compensation, and realizing online self-tuning of model parameters in combination with in-situ measurement feedback. Real-time sensing, full-band dynamic compensation and closed-loop self-adaptive control of multi-source errors are achieved, and the machining precision and long-term stability of the mold are remarkably improved.

The invention relates to the technical field of power supplies, and discloses a high-precision power supply control method based on a digital signal processor, which comprises the following steps of: acquiring data such as voltage, current, temperature, load parameters and the like of a power supply at a high frequency of kHz-level sampling frequency by using a high-precision sensor, transmitting the data to a DSP (Digital Signal Processor), and removing noise interference by applying wavelet transform. Sample quality is improved through high-frequency data acquisition and wavelet denoising, nonlinear and time-varying characteristics of a power supply system are identified by using a lightweight CNN and an LSTM, a reinforcement learning dynamic optimization control strategy is combined, self-adaptive switching between traditional control and an intelligent algorithm is realized through working condition identification, and the power supply system control method based on the LSTM is realized. The problems that noise interference influences data quality, system characteristic recognition is inaccurate, complex working condition control strategy optimization is insufficient, and output precision is unstable due to the fact that a control mode cannot be switched in a self-adaptive mode in an existing power supply can be effectively solved, and control precision, efficiency and device loss can be considered. And the stable operation requirement of the high-precision power supply under multiple working conditions is met.

The invention belongs to the technical field of comprehensive utilization of ocean renewable energy sources, and particularly relates to a pilot frequency coupling-based wind wave energy multi-degree-of-freedom broadband control method, which comprises the following steps of: performing frequency domain identification on wind wave environment characteristics, and constructing a wind wave pilot frequency energy coupling model; based on the motion response characteristics of the multi-degree-of-freedom floating platform, designing a pilot frequency energy regulation and conversion mechanism; a full-working-condition dynamic optimization control strategy is adopted, and cooperative operation and energy flow self-adaptive distribution of the wind energy conversion unit and the wave energy conversion unit are achieved. Aiming at the significant difference and relevance of wind energy and wave energy in frequency characteristics, time scale and spatial distribution, a pilot frequency wind wave coupling dynamic model is established, and broadband capture of low-frequency wind-induced and high-frequency wave-induced coupling energy is realized under complex sea conditions through multi-degree-of-freedom motion decoupling and state observation, so that the wind-induced and high-frequency wave-induced coupling energy is obtained. The energy utilization rate, the attitude stability and the structural safety of the system are effectively improved, and good robustness and engineering adaptability are achieved.

The invention relates to the technical field of force sensors, and discloses a nonlinear decoupling method and system for a three-axis force sensor, and the method comprises the steps: collecting multi-dimensional original data, and carrying out the adaptive noise reduction, dynamic selection of a wavelet basis, and construction of a filtering matrix, and obtaining a noise reduction signal through calculation; constructing a dynamic PINN proxy model, fusing material parameters and environment parameters, outputting a stiffness matrix, establishing an online parameter updating mechanism, calculating the obtained optimization parameters, and constructing an enhanced dynamic compensation matrix; the output stiffness matrix and the enhanced dynamic compensation matrix are fused, and a main singular value is reserved; reconstructing to obtain a reconstructed strain field, establishing a multi-physics field coupling model to compensate quantum-thermal coupling errors, and compensating wire inductive coupling under high frequency; constructing a multi-scale graph structure, strengthening a loss function through physical constraint, performing crystal plasticity hysteresis compensation, fusing multi-scale features, and calculating and outputting a decoupling force value; and an intelligent calibration decision is established, the drift trend is predicted, and the calibration period is dynamically adjusted.

The invention relates to a high-frequency regeneration and rapid propagation method of Enkianthus chinensis tissue culture seedlings. The invention is characterized in that dormant axillary buds of semi-lignified young stems are used as the base. The method comprises the following main steps: sampling and disinfection treatment of explants, inducing of dormant axillary bud germination, inducing of adventitious bud and lateral bud high-frequency generation, strong seedling culture, test-tube rooting culture and acclimatization and transplanting. Finally, regenerative plants which have consistent parental character, excellent quality and strong stress resistance are obtained. According to the invention, lots of seedlings with consistent specification and excellent quality can be obtained within a short period of time, and marketization progress of Enkianthus chinensis is promoted powerfully.

A wireless device may receive one or more configuration parameters indicating entries of a time domain resource allocation table and one or more scheduling offsets. Each entry of the entries may comprise a respective slot offset. The wireless device may receive downlink control information (DCI) indicating a first entry of the entries and a first scheduling offset of the one or more scheduling offsets. The wireless device may receive downlink data in a first slot determined based on a slot offset of the first entry and the first scheduling offset.

The invention provides an intelligent optimization control method for the energy consumption of a packaging production line, and the method comprises the steps: collecting the processing data of the production of products of two specifications of vacuum can sealing and normal pressure can sealing, extracting the load change of a conveying belt during the switching of different specifications, and obtaining the fluctuation range of the load of the conveying belt; predicting the operation power scheduling of the equipment according to the instantaneous peak value and high-frequency fluctuation characteristics of the power demand, and outputting energy consumption peak value prediction data of the equipment during specification switching; determining and outputting an operation time sequence plan according to the energy consumption peak prediction data of the equipment during specification switching; monitoring the dynamic change of the conveyor belt load in real time according to the operation time sequence plan, obtaining the deviation value of the current load and the predicted load, and judging whether the deviation value affects the energy consumption distribution mode of the whole production line or not; and through the new energy consumption distribution result, generating and executing an equipment cooperative operation control instruction in combination with the operation time sequence plan.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a laser frequency stabilization system based on Faraday anomalous dispersion atomic filtering, which is used for carrying out frequency stabilization on phase modulation multi-sideband laser, and comprises a frequency doubling laser unit which is configured to comprise a fundamental frequency seed light source, an electro-optical modulator, a fundamental frequency amplifier and a frequency doubling crystal; the light splitting module unit is configured to split the frequency doubling light into a main output light path and a monitoring light path; the atomic optical rotation filter is located on the monitoring light path and is configured to maintain the purity of the frequency-stabilized laser spectrum through purification treatment under the control of the axial magnetic field and the temperature; the frequency stabilization unit is configured to be a frequency stabilization light path and a servo system, and the frequency stabilization light path is used for obtaining laser frequency information and generating an error signal; the servo system is used for controlling the frequency of the fundamental frequency seed light source according to the error signal feedback. According to the laser frequency stabilization system provided by the invention, the spectrum purification and frequency stabilization precision can be improved, so that high-frequency stability locking is realized.

The invention belongs to the technical field of artificial intelligence, and relates to a distributed photovoltaic system abnormity monitoring and intelligent early warning method. Key parameters of the distributed photovoltaic system are collected at high frequency, noise filtering and signal normalization processing are carried out on data, then multi-domain feature extraction is carried out, anomaly detection is carried out through auto-encoder reconstruction error analysis, convolutional neural network feature classification and a single-class support vector machine, and an anomaly detection result is output. Establishing a multi-dimensional anomaly evaluation model, and synthesizing obtained evaluation results of all dimensions to obtain a final evaluation result; the method comprises the following steps: carrying out hierarchical classification to identify specific anomaly types, carrying out diagnosis of different types of anomalies, identification of typical anomalies and evaluation of fault severity, outputting the specific anomaly types and severity, and providing detailed diagnosis information for early warning decision making. The system state is comprehensively evaluated, corresponding measures are taken according to the grading early warning mechanism and the abnormity severity degree, and complete distributed photovoltaic system abnormity monitoring and intelligent early warning are formed.

The invention discloses a current source type LCCL double-frequency resonance system and a parameter matching method thereof, which are suitable for synchronous double-frequency induction quenching of complex workpieces such as gears. The system is composed of two current type resonant inverters working at a low frequency and a high frequency respectively and a dual-frequency LCCL resonant cavity, and dual-frequency current superposition is realized through parallel output. The double-frequency LCCL resonant cavity has two parallel resonance points, a mode based on simplified topology is adopted to simplify parameter design, and meanwhile design errors are maintained at a low level. A power matching coefficient is introduced, the ratio of high-frequency output power to low-frequency output power is adjusted by adjusting the value of the power matching coefficient, and the problem of double-frequency power unbalance caused by frequency variation characteristics of load equivalent resistance and the like is solved. The superposed double-frequency current forms a low-frequency envelope embedded high-frequency oscillation composite waveform on the induction heating coil, cooperative control of tooth root deep quenching (low frequency) and tooth surface hard quenching (high frequency) is achieved, and the method has the advantages of being simple in parameter design, high in power decoupling performance, high in operation stability and the like.

The embodiment of the invention discloses an insulation aging early warning method and device for a medium-high frequency transformer and a medium, and relates to the technical field of transformers, and the method comprises the steps: collecting a multiband light intensity distribution signal of a target resin region in the transformer according to a preset collection frequency through a preset multispectral distributed optical fiber sensing array; the insulation state of the target resin area is predicted through a pre-constructed neural network identification model and a multi-spectral distributed optical fiber sensing array, and the predicted insulation state health degree of the target resin area is determined; acquiring real-time environment parameters of the target resin area, dynamically correcting a preset early-warning discharge threshold according to the real-time environment parameters, and determining a current early-warning dynamic discharge threshold; and extracting a plurality of current light intensity parameters in the multiband light intensity distribution signal, predicting an insulation state health degree and a current early warning dynamic discharge threshold through the plurality of current light intensity parameters, and performing insulation defect early warning on the insulation state of the target resin area.

The invention discloses a high-time-resolution flow field testing method and system based on sparse moment measured values and an intelligent power system, and belongs to the field of flow field testing and data reconstruction in bridge wind engineering. According to the method, overall low-frequency and local high-frequency sparse flow field data are obtained through a four-pulse fixed-frequency variable-frequency laser system and a four-exposure high-speed imaging PIV sampling system; a 32-dimensional nonlinear modal coefficient is extracted through a multi-scale convolution flow field sparse feature extraction model, then an unsampled time step coefficient is predicted through an LSTM intelligent power system model, and finally the unsampled time step coefficient is input into a multi-scale convolution auto-encoder to reconstruct a high-time-resolution flow field. The method does not need to depend on a pressure sequence, reduces the hardware and data processing cost through sparse sampling, accurately captures the flow field dynamics characteristics through intelligent modeling, solves the problems of expensive high-frequency hardware, loss of low-frequency reconstruction data and difficulty in model training in a traditional PIV test, and is suitable for flow field dynamics research and engineering optimization.

Plug connectors for use with high speed signals. The connector includes an insulative housing having a base portion and a tongue portion extending from the base portion, and conductive terminals held by the housing. Each conductive terminal has a mating portion held on a side of the tongue portion, a tail portion extending out of the housing, and an intermediate portion held in the base portion. The conductive terminals comprise signal terminals and ground terminals. A shielding member is disposed in the housing and includes features configured to provide shielding to signal terminals and electrically connect ground terminals. The shielding member also includes features configured to improve impedance consistency along conduction paths. Such a configuration meets signal integrity requirements in connectors designed for higher frequencies, while conforming to a standard that constrains mating and mounting interfaces.

The invention belongs to the technical field of testing equipment, and particularly relates to a multipurpose high-frequency fatigue testing machine which comprises main control equipment and a fatigue testing machine body, the fatigue testing machine body comprises a bottom machine and an electromagnetic high-frequency generating machine body, and a balance weight table is assembled under the electromagnetic high-frequency generating machine body through connecting rod connection; clamp assemblies are assembled on the surface of the bottom machine and the lower surface of the counterweight table; the multiple balance weights are adjustably stacked on the surface of the balance weight table and used for being matched with the resonance points by replacing balance weights; and the plate clamp is detachably assembled with the clamp assembly and is used for detecting cracks generated in the test process of the test sample. The device can replace manpower to flexibly adjust the counterweight, realizes rapid and convenient operation, can timely detect a cracked plate in a test, and is convenient to timely stop the test before the sample is fractured.

The invention relates to the technical field of industrial vision, in particular to an automobile part surface defect detection method and system based on machine vision, and the method comprises the steps: firstly loading a CAD three-dimensional model, and rendering an ideal fringe reflection map in a virtual environment; calculating an optical distortion correction matrix by comparing the phase distortion with the phase distortion of an actual initial reflection image, driving a programmable light source to project a compensation pattern, and generating a normalized reflection intensity image; signal object decoupling is achieved through multi-scale wavelet transform, and high-frequency components and low-frequency components are separated; homomorphic filtering is applied to the low-frequency component to construct a homogenized background model, the high-frequency component is reversely corrected, and a high-signal-to-noise-ratio defect signal image is output; generating a coarse segmentation mask by the high-frequency signal through an adaptive local threshold, generating a morphology anomaly mask by the low-frequency signal through Hessian matrix morphological analysis, and fusing to form a collaborative segmentation mask; and extracting multi-dimensional geometric attributes of connected domains in the masks, and inputting the multi-dimensional geometric attributes into a decision tree to realize accurate classification and confidence output of defects.

The invention provides a wave finite element and boundary element mixed composite board structure sound insulation performance prediction method, and the method belongs to the acoustics field, and comprises the steps: carrying out the post-processing of a wave finite element model based on the wave finite element model of a composite board structure and a corresponding equivalent nodal force, and obtaining the wave finite element equation of the whole composite board structure; based on a sound-structure coupling equation, through a boundary element method, a discrete boundary element equation related to the surface vibration speed of the composite board and the radiation sound wave pressure is constructed; coupling the wave finite element equation and the discrete boundary element equation of the whole composite board structure, and determining the sound pressure of radiation sound waves of the composite board structure and the surface vibration speed of the composite board; and based on the sound pressure of the radiation sound wave of the composite board structure and the surface vibration speed of the composite board, obtaining a sound insulation performance prediction result by calculating the sound power of the incident sound wave and the radiation sound wave, and completing prediction of the sound insulation performance of the composite board structure. The problems that the sound transmission loss of large plates and medium-high frequency plates is difficult to predict and the calculation cost is high are solved.

The invention discloses a GIS ultrahigh frequency sensor ultrasonic cooperative detection system based on piezoelectric energization, and relates to the field of electric power detection, and the system comprises an excitation module which is used for transmitting a piezoelectric excitation signal with a specific frequency to a preset detection region in GIS equipment, so as to trigger the potential partial discharge acoustoelectric signal radiation of the region; the sensing module is used for receiving ultrahigh-frequency electromagnetic signals and ultrasonic vibration signals radiated by partial discharge in the GIS equipment; the excitation frequency, the filtering bandwidth and the sampling rate are dynamically optimized according to the detection result, the detection accuracy and efficiency are continuously improved, and reliable and effective guarantee is provided for safe operation and defect early warning of GIS equipment.

The invention discloses a handcart type high-voltage circuit breaker operating characteristic evaluation method based on big data analysis, and particularly relates to the field of data analysis, which comprises the steps of monitoring time division, data entry, data analysis and evaluation. All-time multi-dimensional monitoring is achieved through big data analysis, monitoring sub-areas are divided, dynamic data are collected at high frequency, transient changes are captured, the limitation of traditional single-point monitoring is broken through, and comprehensive data support is provided; quantitative indexes are constructed based on a multi-parameter fusion model, the electrical and mechanical system collaboration is comprehensively evaluated, the multi-dimensional performance is reflected, and the evaluation precision is improved; and a health interval judgment rule is established, so that real-time comprehensive evaluation can be realized, faults can be accurately positioned, anomalies can be recognized in advance, a basis is provided for maintenance, and the intelligent level of power operation and maintenance and the fault prevention capability are improved.

The invention discloses a 5G high-frequency signal anti-interference flexible cable which comprises a first cable and a second cable, bare cells of the first cable and the second cable are stranded and then connected through tin soldering, a sliding sleeve sleeves the bare cells together, the bare cells in the sliding sleeve sequentially wrap an insulating sealing sleeve, a metal shielding net, insulating rubber, a buffer pad and a sheath, the sheath is fixed to the inner wall of the sliding sleeve, and the inner diameter of the sheath is larger than the outer diameter of the cable. The two ends of the sliding sleeve clamp the cable through elastic clamping pieces. The metal shielding net is woven by high-conductivity copper and is welded with the original shielding layer of the cable to form a continuous shielding channel, so that electromagnetic interference is effectively blocked; the inner wall of the insulation sealing sleeve is provided with a spiral sealing groove consistent with the cable twisting direction, and the spiral sealing groove is matched with insulation rubber, a buffer pad and a sheath with a sealing ring, so that the sealing performance is enhanced, and high-frequency interference is attenuated; the design of the sliding sleeve and the elastic clamping piece enables the shielding structure to be complete when the cable is bent. The cable ensures stable transmission of 5G high-frequency signals through multi-layer shielding and flexible structure design, and is suitable for high-frequency communication scenes requiring interference resistance.

The invention relates to a long-gap discharge leader development speed measurement method based on radiated electromagnetic wave detection. The method comprises a set of high-frequency antennas and a high-speed oscilloscope and a long-gap discharge leader development speed calculating method. The method adopts a certain antenna arrangement form; long-gap discharge radiated electromagnetic wave pulse time is detected simultaneously through the plurality of antennas; leader head position is positioned according to time difference of a signal arriving at different antennas; and furthermore, the long-gap discharge leader development speed can be calculated. The method can effectively improve the measurement precision of the discharge leader development speed, and resolution reaches the level of 0.1 ns.

The invention discloses a power carbon emission measurement and calculation data anomaly identification method and system. The method comprises the steps of obtaining power carbon emission original data and performing preprocessing; the method comprises the following steps: decomposing data into a plurality of internal mode functions and residual sequences by adopting an adaptive variable scale weighted noise enhancement CEEMDAN method, and effectively separating a time sequence trend from high-frequency fluctuation; constructing an improved box plot model based on the residual sequence, and adaptively adjusting an abnormal boundary by introducing a median deviation coefficient and a dynamic abnormal detection coefficient; and identifying abnormal data points by using the optimized boundary, and performing correction or elimination processing. According to the method, the problem that a traditional method is poor in adaptability to non-stationary and multi-scale electrical carbon data is solved, normal periodic fluctuation is prevented from being misjudged to be abnormal, the accuracy and robustness of anomaly detection are remarkably improved, and a reliable data basis is provided for accurate measurement and calculation of carbon emission of an electric power system.

A midboard cable connector assembly with a board connector and a cable connector. The board connector has terminals precisely positioned with respect to engagement features. A cable connector likewise has terminals precisely positioned with respect to complementary engagement features. When the connectors are pressed together for mating, the terminals of one or both connectors deform, generating a force that separates the connectors until the engagement features engage and block further backward motion. As the mating position is defined by the locations of the engagement features, the connectors can be designed with low over travel and a resulting short stub length, which promotes high frequency performance. High frequency performance is further promoted by a ground conductor interconnecting the beams forming mating contact portions of ground terminals, reducing the length of unconnected segments and by a cable clamp plate with complaint compression features that reduce distortion of the cables.

The invention relates to the technical field of wind field construction, and provides a low-altitude three-dimensional real-time wind field construction method and system based on unmanned aerial vehicle data inversion, and the method comprises the steps: collecting the multi-dimensional data of a flight attitude, a power system, navigation positioning and the like in real time through an unmanned aerial vehicle group in the process of executing a distribution task; and extracting key indexes such as attitude change features, control response deviation features, energy consumption features and the like from the data, inputting the features into a deep neural network with a physical model as a constraint to carry out wind field inversion training, and finally carrying out multi-scale spatio-temporal interpolation on discrete observation points through a Gaussian process regression algorithm fused with the physical constraint. And generating continuous three-dimensional wind field distribution meeting the law of conservation of mass. Dynamic wind field monitoring under urban complex terrains is realized by utilizing the characteristics of high-frequency and wide-coverage operation of the unmanned aerial vehicle, and the problem of insufficient observation data of a traditional weather station in a building dense area is effectively solved.