117 results about "Nonlinear calibration" patented technology

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention discloses a GPS interference phased array monitoring direction-finding antenna system for a 1575 frequency band, and relates to the technical field of intelligent antennae, comprising a data integration module for integrating data of satellite ephemeris, carrier attitude and airspace coverage through a weighted average method and generating an environment parameter set, a prediction calibration module for predicting the data of the satellite ephemeris, the carrier attitude and the airspace coverage through a weighted average method and generating an environment parameter set, and a direction-finding antenna module for performing direction-finding on the environment parameter set. The phase drift prediction module is used for inputting an environment parameter set into a bidirectional LSTM network, obtaining a phase drift prediction value, carrying out nonlinear calibration on the phase drift prediction value by adopting a Kalman-particle hybrid filtering algorithm, and synchronously monitoring an IQ signal by utilizing a closed-loop feedback mechanism, and the beam generation module is used for carrying out orthogonal modulation and radio frequency up-conversion processing on the IQ signal, and outputting the processed IQ signal. According to the method, the bidirectional LSTM network is combined with the Kalman-particle hybrid filtering algorithm, so that phase drift caused by factors such as carrier vibration and the like can be accurately predicted and calibrated in real time.

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.

The invention relates to the technical field of data correction, in particular to a humidity sensor nonlinear correction method, which comprises the following steps of: performing pairwise matching based on ambient humidity initial data and ambient temperature initial data of a humidity sensor, constructing a polynomial fitting initial curve, and utilizing a measurement deviation value of each group of data to obtain a nonlinear correction result; and correcting each curve node parameter in the polynomial fitting initial curve, and generating a humidity and temperature fitting initial parameter group. According to the method, the curvature characteristic and the offset characteristic are analyzed through the Laguerre polynomial curve model, different data segments are distinguished, fine compensation adjustment is carried out, the precision of nonlinear correction is remarkably improved, the real-time correction effect of the measured value is optimized by combining the difference value operation of the curve node parameters and the real-time measured value and dynamic offset trend analysis, and the accuracy of nonlinear correction is improved. The overlay analysis of the correction compensation coefficient and the dynamic offset further improves the humidity measurement stability and accuracy in a complex environment.

The invention discloses a nonlinear correction method, device and equipment for excitation characteristics of a mutual inductor, and particularly relates to the technical field of mutual inductors. The method comprises the following steps: acquiring original data corresponding to a mutual inductor, wherein the original data comprises an original current signal, historical magnetic flux and distributed temperature data; performing noise reduction processing on the original current signal to obtain a purified current signal, and determining a magnetic conductivity correction coefficient and a temperature gradient according to the distributed temperature data; inputting the purification current signal and the magnetic conductivity correction coefficient into a hysteresis model to obtain a real-time magnetic flux, and inputting the purification current signal, the distributed temperature data, the temperature gradient and the historical magnetic flux into a magnetic flux correction network to obtain a magnetic flux correction value; and compensating the real-time magnetic flux according to the magnetic flux correction value to obtain a corrected magnetic flux result of the mutual inductor. According to the invention, multi-physical field fusion input can be realized, transient response can be improved, harmonic distortion compensation can be carried out, and high precision of data can be maintained.

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent security and protection alarm method and system based on an intelligent park. The method comprises the following steps: acquiring original security and protection data by using sensing equipment arranged in a smart park; performing standardization and redundancy elimination processing on the original security and protection data, dividing safety areas of the park through a pattern recognition algorithm, calculating a load intensity index of each area, and generating a security and protection situation summary; according to the security situation summary, a heuristic search algorithm is adopted to determine alarm trigger parameter initial values including detection sensitivity and alarm intervals, and a basic alarm parameter group is generated; performing nonlinear correction on the basic alarm parameter group, and outputting an environment perception alarm parameter group; training the historical security data set by adopting a neural network model, and analyzing the interaction between the load and the threat to obtain a threat diffusion model; and comparing the threat diffusion model with the real-time security data points, performing difference measurement, reconstructing alarm trigger logic according to a measurement result, and completing autonomous regulation and control of security and protection of the smart park.

The invention discloses a gas sensor multi-mode adaptive preprocessing method based on a Mama state space model, and relates to the field of gas detection, and the method comprises the following steps: synchronously collecting original signals, environmental parameters and historical time sequence data of a sensor; performing feature extraction on the three types of data to generate a sensor feature vector, an environment feature vector and a historical feature vector; the method comprises the following steps of: obtaining a final fusion feature through processing, mapping the final fusion feature to a 128-dimensional embedding space through a contrast learning encoder, inputting a three-layer residual error connection Mamba time sequence prediction network to output a zero offset, dynamically predicting process noise and observation noise through a neural network, executing adaptive Kalman filtering to output a smooth signal, and outputting a final fusion signal. Adopting a learnable piecewise linear network to carry out nonlinear correction on the filtering signal; the gas concentration value is calculated by integrating the correction signal and the zero offset, and finally a confidence score and a data quality mark are provided; according to the method, high-precision, real-time and robust pretreatment of various gas sensors in a complex environment is realized.

The invention discloses a Rhatish culture VRAR virtual-real fusion interaction method based on eye movement tracking, and particularly relates to the technical field of virtual reality and augmented reality man-machine interaction. The method comprises the following steps: acquiring an eye image of a user through a near-infrared image acquisition unit, and inputting the eye image into a double-branch convolutional neural network model to estimate a two-dimensional fixation point of a screen; performing nonlinear correction on a fixation point based on the user personalized sight line offset correction model established in the offline calibration stage; in the online interaction stage, the corrected fixation point is combined with equipment pose data to be converted into three-dimensional space rays, the distance and angle relation between the three-dimensional space rays and an interaction target is calculated, and an interaction instruction is triggered when a threshold condition is continuously met. According to the method and the system, accurate, stable and natural eye movement interaction of the virtual contents of the Rhatish culture and the entity exhibits in the VR / AR environment is realized, and the user experience and the system robustness are improved.

A powder particle grading and particle size distribution statistical system of the present invention belongs to the technical field of particle measurement, and comprises: a multi-dimensional parameter acquisition module for acquiring physical parameters of powder particles to be measured; the coupling state judgment module is used for calculating a critical state index based on the physical parameters acquired by the multi-dimensional parameter acquisition module; the nonlinear correction model module is used for receiving the original particle size measurement data and the critical state index of the external laser diffraction measurement unit and calculating the original particle size measurement data based on a preset nonlinear correction model to generate corrected particle size data; and the particle size distribution statistical module is used for performing statistical analysis on the corrected particle size data and outputting a final particle size distribution report, and the method greatly improves the pertinence and accuracy of measurement.

The invention discloses a remote monitoring system for an atomizing furnace, belongs to the technical field of remote monitoring of the atomizing furnace, and solves the problems that inert gases such as argon are large in molecular weight and easy to form laminar flow in a pipeline, a traditional turbine flowmeter is based on turbulence calibration, the laminar flow causes non-uniform flow velocity distribution, the linear relation between the rotating speed and the flow is destroyed, and the reliability is poor. And the system misjudges that the flow is insufficient. Comprising a data acquisition module, an abnormity monitoring module, a data processing module and a wireless communication module, and the data acquisition module is used for acquiring temperature, pressure, liquid and gas flow and atomization effect in the furnace and feeding back the temperature, pressure, liquid and gas flow and atomization effect to the data processing module. Flow field characteristics are extracted through multi-sensor fusion, a generalized gas physical property model is constructed, and precise flow measurement is achieved by combining segmented nonlinear correction and vibration suppression; a multi-target robust controller and a dynamic feedforward-feedback collaborative optimization mechanism are designed, and the problem of control instability in a laminar flow / turbulent flow mixed state is solved.

The invention provides an intelligent traffic task execution method and device based on image defogging. The method comprises the steps of collecting a road image and calculating atmospheric light of the road image. And screening out the minimum value of a plurality of color channels of each pixel point in the road image to obtain a minimum color channel. And calculating the haze concentration of the road image, introducing a correction coefficient, constructing a nonlinear correction function, and mapping the haze concentration by using the nonlinear correction function to obtain the transmissivity. And inputting the transmissivity and the atmospheric light into an atmospheric scattering model for inversion so as to remove haze in the road image and obtain a defogged image. And inputting the defogged image into an intelligent traffic system, and executing an intelligent traffic task. The defogging algorithm adopted by the invention does not depend on mass data, is efficient in calculation and is suitable for complex real scenes, and the intelligent traffic task execution method based on the defogging algorithm can accurately carry out license plate recognition and target tracking.

The invention discloses a rotor turn-to-turn short circuit diagnosis method based on an exciting current-shaft voltage harmonic nonlinear correlation coefficient. The method comprises the following steps: synchronously collecting parameters, and preprocessing the collected parameters through wavelet threshold denoising and temperature normalization; extracting harmonic analysis according to the shaft voltage harmonic characteristics; identifying magnetic saturation, temperature and load nonlinear factors and calculating corresponding coefficients; performing dynamic correction on the Pearson's correlation coefficient through the nonlinear correction coefficient, and combining with fuzzy logic weighting to obtain fuzzy logic weighting; defining a working condition feature vector, and constructing a working condition adaptive dynamic model comprising a linear regression basic model and an LSTM neural network; and calculating a dynamic threshold value based on the basic threshold value and the nonlinear factor, and judging the turn-to-turn short circuit trend of the rotor according to the comparison of the correlation coefficient and the dynamic threshold value and a prediction result. According to the invention, through combination of the nonlinear correction correlation coefficient and the LSTM neural network, dynamic compensation of nonlinear factors is realized, and the accuracy and reliability of rotor turn-to-turn short circuit fault diagnosis are improved.

The invention relates to the technical field of temperature sensor control, in particular to a nonlinear correction optimization method for a miniature semiconductor temperature sensor based on deep learning, which comprises the following steps: acquiring an original temperature sequence of the miniature semiconductor temperature sensor in a preset temperature range, and synchronously acquiring an error related environmental parameter sequence and a sensor aging coefficient; the original temperature sequence is subjected to empirical mode decomposition processing, and main temperature fluctuation mode components including nonlinear errors are extracted, the original temperature signals of the miniature semiconductor temperature sensor are subjected to hierarchical processing through empirical mode decomposition, the main temperature fluctuation mode components carrying the nonlinear errors can be accurately separated and extracted, and the accuracy of temperature fluctuation is improved. In combination with the dual-branch neural network processing data and through the null drift compensation factor and the sensitivity compensation factor, baseline drift and sensitivity attenuation caused by long-term use are corrected in a targeted manner, so that the miniature semiconductor temperature sensor still keeps high precision after long-term use, and the application in a high-precision scene is met.

The invention provides a camera monitoring video real-time identification data processing method and system, and relates to the technical field of computer vision and intelligent monitoring, and the method comprises the steps: extracting a current video frame from a real-time video frame sequence based on a multi-dimensional environment state descriptor, executing adaptive scene partitioning, and obtaining the confidence coefficient weight of each region; taking the confidence coefficient weight of each region as input, and performing joint nonlinear calibration on core parameters of the target detection network through a lightweight neural network model to obtain an optimized detection parameter set; and carrying out multi-target synchronous identification and depth feature abstraction on the current video frame by adopting the optimized detection parameter set to obtain a virtual feature contour set containing personnel and vehicles. Through environment adaptive detection and depth feature abstraction, precise identity verification, abnormal behavior recognition early warning and cross-domain trajectory association and cooperative response, a complete intelligent processing link from complex scene perception to global situation understanding and closed-loop management and control is realized.

The invention discloses an FMCW laser measurement system light source nonlinear correction method based on physical mapping modeling and a TD3 algorithm, and belongs to the fields of laser radar, laser measurement, artificial intelligence and the like. An FMCW laser measurement system is usually limited by the nonlinear characteristic of a tunable laser, especially the bandwidth is increased under triangular wave modulation, a traditional correction method is difficult to achieve a good effect, and the measurement precision and quality are affected. The invention provides an innovative correction method combining a physical mapping model and a TD3 algorithm, and provides an effective compensation mechanism for nonlinear errors of an FMCW laser measurement system under triangular wave modulation. Comprising the following steps that an FMCW laser measurement system is designed and built, PD measurement data are utilized, data preprocessing is carried out, and a data set is built; fitting nonlinear characteristics of the system by using a physical mapping relation; nonlinear correction is realized based on TD3 algorithm control; the nonlinear correction method optimizes the performance of the FMCW laser measurement system through combined application of physical mapping modeling and an RL control strategy.

The invention provides artificial intelligence-based characteristic guidance method and system for enhancing quality of a diffusion model in generating a data from a noisy data based on a condition information. The method comprises: generating a nonlinear precorrection vector; performing, by a regularization module, a context regularization iteration to obtain an updated nonlinear correction vector and a nonlinear correction gradient; checking if a convergence criterion is met; and denoising the noisy data based on the updated nonlinear correction vector to generate the data. By using the regularization module, the provided characteristic guidance method not only greatly improves the stability of data generation by the diffusion model, but also provides enhanced control over context through two context modes: the detail enhancement mode and the context enhancement mode. The present invention can enhance the semantic characteristics of prompts and mitigate irregularities in image generation.

The invention relates to the technical field of earphone equipment, in particular to a Bluetooth earphone non-contact type dynamic control system and method based on gesture recognition, and the system comprises an induction signal collection module which obtains the total number of clock cycles and generates an original count value, a signal standardization module which carries out the operation of the original count value to generate a standardization intensity value, and a control module. The spatial feature distinguishing module is used for generating a spatial curvature gradient coefficient based on the standardized intensity numerical value and screening an effective intensity numerical value; and the linear mapping control module is used for performing nonlinear correction mapping on the effective numerical value, generating a vertical distance coordinate numerical value and generating a volume gain control parameter based on a change rate. According to the method, environment interference is eliminated through standardized data differential operation, effective fingertip control gestures and large-area skin contact are distinguished through space curvature gradient coefficients, a polynomial regression model is called to establish the corresponding relation between signal intensity and distance coordinates, non-contact hovering interaction is achieved, and ear canal compression and mechanical abrasion are avoided.

The invention relates to the technical field of blood glucose monitoring, in particular to a CGM sensor calibration method and system based on a non-linear compensation model.The method comprises the steps that in-plant verification sensitivity of a sensor serves as a core input parameter of a non-linear calibration function, and inherent response characteristics of the sensor when the sensor leaves a factory are included into a blood glucose estimation model; the sensitivity reference of each sensor is explicitly quantified, so that the source of individual difference of the sensors is eliminated by verifying sensitivity priori in an embedded factory, and the same current and different blood glucose caused by initial difference of sensor production process and enzyme activity can be avoided; compared with the prior art that the sensitivity difference when different CGM sensors leave a factory is not considered, cross-sensor signals and blood glucose mapping references can be unified from the root of a model only through a unified linear function or depending on SMBG manual calibration adaptation, and the technical problem that cross-sensor consistency and individualized adaptability are poor is solved.

The invention is suitable for the technical field of laser radars, and provides a radar high-frequency segmented correction device and method.The device comprises a scanning component, a receiving component and a correction component, and the correction component is used for obtaining beat frequency signals corresponding to all receiving light paths according to modulated waves and reflected waves received by all the receiving light paths; sampling each beat frequency signal to obtain a plurality of sampling correction time domain signals, intercepting a segment with a smooth time domain in each sampling correction time domain signal, and splicing all segments according to a time axis in a full modulation period to form an updated sampling correction time domain signal; fourier transform is carried out on the updated sampling correction time domain signal to obtain a single-frequency spectrum peak graph; and performing frequency modulation nonlinear correction on the radar by using the spectral peak map. According to the device, modulated waves are divided into multiple paths, and different lengths are set for optical fibers in multiple receiving optical paths to generate different phase shift effects, so that segmented correction of different frequencies is realized, and the distance measurement precision and stability are improved.

The invention relates to the field of gamma dose rate monitoring, and provides a high-altitude radioactive point source dose rate distribution analysis method and system, and the method comprises the steps: collecting original counting data and energy spectrum data, and obtaining environment parameter data and flight carrier position information; performing statistical calculation on the original counting data to obtain counting statistical parameters; performing nonlinear correction on the original counting data, the energy spectrum data, the environmental parameter data and the counting statistical parameters to obtain corrected detector data and measurement uncertainty; performing fusion calculation on the corrected detector data to obtain a fusion weight, and performing verification processing to obtain fusion dose rate data; performing prediction processing on the fused dose rate data and the environmental parameter data to obtain a range switching signal; and performing reconstruction processing on the fused dose rate data, the range switching signal, the measurement uncertainty and the flight carrier position information to obtain ground dose rate distribution data. According to the invention, the accuracy and reliability of high-altitude radioactive point source dose rate distribution analysis are improved.

The invention discloses a flue gas waste heat gradient utilization system optimization method based on deep transfer learning, and the method comprises the steps: firstly constructing a flue gas waste heat system simulation model under multiple working conditions based on an EBSILON simulation platform, and generating a simulation data set covering flue gas waste heat system key operation parameters of different loads, environment conditions and the like; then, establishing a deep neural network pre-training model based on simulation data, adding linear and nonlinear calibration networks on the basis of the deep neural network pre-training model, performing hyper-parameter updating on the calibration networks by using field operation data, and migrating the pre-training model to a feature space adaptive to the operation data; on the basis, flue gas bypass parameters are dynamically adjusted in combination with a genetic algorithm, and maximum utilization of waste heat resources is achieved. According to the method, the limitation of scarcity of field effective operation data samples on model training is overcome, and the overall energy efficiency of the system is improved through intelligent regulation and control.

The application discloses a nonlinear calibration method and device of a magnetic encoder. The method comprises the following steps: setting multiple inflection points on an angle curve diagram formed based on an angle scanning result to divide an angle period into multiple angle curve segments, wherein each angle curve segment is approximately a first-order curve; acquiring a compensation coefficient of each angle curve segment; acquiring an angle output threshold value of each angle curve segment; and starting calibration after detecting a data valid signal, acquiring a target output angle after calibration based on the corresponding compensation coefficient and the angle output threshold value according to an input angle. The application divides a complete angle period into multiple angle curve segments by setting multiple inflection points, and each angle curve segment is approximately a first-order curve. Then, the slope of each angle curve segment is approximated to 1 through a compensation coefficient, and a target output angle compatible with an angle measurement value of an angle measuring instrument is obtained after calibration. The compensation algorithm is simplified, and resource consumption is reduced.

Methods, apparatus, systems, and articles of manufacture to correct non-linearity in a transmitter are described. An example system includes: an input stage (202); a driver (204), an input terminal of the driver (204) being coupled to an output terminal of the input stage (202); an output buffer (208), an input terminal of the output buffer (208) being coupled to an output terminal of the driver (204), an output terminal of the output buffer (208) being coupled to a first input terminal of the input stage (202) via a resistor (212); and a non-linear correction circuitry (218) having an input terminal and an output terminal, the input terminal of the non-linear correction circuitry (218) being coupled to the output terminal of the output buffer (208) and the first input terminal of the input stage (202) via the resistor (212), the output terminal of the non-linearity correction circuitry (218) is coupled to the output terminal of the input stage (202) and the input terminal of the driver (204).

This invention discloses a system and method for monitoring partial discharge and thermal characteristics of switchgear, comprising: a dual-spectrum imaging module that simultaneously acquires a sequence of ultraviolet pulse images and an infrared temperature field sequence; a spatiotemporal synchronization control module that generates a synchronization control signal and acquires operating condition information; and an edge fusion diagnosis module deployed at a local edge that extracts pixel-by-pixel features from the spatiotemporally aligned dual-spectrum images, constructs a joint feature vector matrix of partial discharge and temperature rise, and performs real-time nonlinear correction based on the operating condition feature vector by retrieving an operating condition feature correction model, then retrieves the spatiotemporal weight allocation rule corresponding to the current defect evolution stage, dynamically allocates spatial weights and temporal weights for weighted fusion, generates a fusion confidence heatmap, and thus achieves defect identification and level classification. This invention achieves accurate diagnosis of insulation defects in switchgear through a collaborative mechanism of adaptive operating condition correction and dynamic weight fusion.

An embodiment of the present invention provides a soil nutrient spectrum detection and regulation method and system, belonging to the technical field of soil nutrient spectrum detection and regulation. The method includes: collecting soil multispectral raw data and environmental data, generating a calibration matrix and nonlinear correction parameters, performing optical reflectance conversion, and synchronously fusing the time-domain reflectometry dielectric constant and the spectral moisture index to obtain a fused moisture content; selecting a nutrient diagnostic characteristic band pair to obtain a moisture-suppressed spectral index; using a pre-trained transfer learning model to predict soil nutrient content, obtaining a soil nutrient content prediction value, and performing fertilization regulation based on the fertilization decision risk coefficient. The present invention addresses extreme soil moisture measurement deviations through soil type adaptive moisture fusion technology; through multi-source risk quantification decision-making and PID adaptive regulation, it can dynamically optimize fertilizer application and ensure execution safety, forming a precision agriculture closed loop of detection-decision-execution.

The present invention discloses a GPS interference phased array monitoring and direction-finding antenna system for the 1575 frequency band, relating to the technical field of smart antennas. The system comprises a data integration module for integrating satellite ephemeris, carrier attitude, and airspace coverage data using a weighted averaging method to generate an environmental parameter set; a prediction and calibration module for inputting the environmental parameter set into a bidirectional LSTM network to obtain a phase drift prediction value, and performing nonlinear calibration on the phase drift prediction value using a Kalman-particle hybrid filtering algorithm, while simultaneously monitoring IQ signals using a closed-loop feedback mechanism; and a beamforming module for performing orthogonal modulation and radio frequency up-conversion processing on the IQ signals, and generating a Butler matrix beam using a hybrid topology beam space transformation algorithm. The present invention utilizes a bidirectional LSTM network in combination with a Kalman-particle hybrid filtering algorithm to accurately predict and calibrate phase drift caused by factors such as carrier vibration in real time.

The invention provides a regional integrated energy system material configuration prediction method and system based on a regression algorithm, and relates to the technical field of energy configuration prediction.The method comprises the steps that historical operation data, environment data and equipment operation data of a regional integrated energy system are collected; carrying out time sequence fusion on the transportation volume, the coal yield and the generating capacity data to form a plurality of time sequence vectors; clustering analysis is carried out on the time sequence vectors to extract typical load curve features and generate clustering center feature vectors, and meanwhile key influence factors are determined based on historical data; and in combination with the clustering center feature vector and the key influence factor, generating a preliminary prediction result, finally performing nonlinear correction, and outputting a configuration prediction scheme conforming to the physical constraint of the regional energy network. According to the invention, the accuracy and adaptability of material allocation prediction of the regional integrated energy system are improved.