255 results about "Threshold function" patented technology

The invention discloses a pediatric abnormal breathing detection method and system based on a physiological signal, and relates to the technical field of medical information processing, and the method comprises the steps: collecting a multi-modal physiological signal, employing wavelet decomposition and a soft threshold function to eliminate noise, and outputting a preprocessing signal group in combination with morphological opening and closing operation; calculating a time domain feature, a frequency domain feature and a nonlinear feature of the preprocessed signal group, organizing the features into a three-layer pyramid structure, analyzing time sequence similarity by adopting an attention mechanism, and outputting a fusion feature sequence; and carrying out preliminary anomaly detection on the fused feature sequence, carrying out classification based on an age grouping threshold, extracting context feature vectors of windows before and after an abnormal point, and carrying out secondary discrimination to realize accurate identification and graded early warning of the respiratory anomaly. The respiration monitoring system with high reliability, high adaptability and intelligent learning ability is constructed, and comprehensive technical support is provided for early recognition, real-time early warning and intelligent diagnosis of children respiratory system diseases.

The invention discloses a short-term load prediction method and system based on improved GWO-VMD-LSTM, and the method comprises the steps: carrying out the parameter optimization of a mode decomposition number and a penalty factor which need to be determined before VMD feature extraction, carrying out the variational mode decomposition of a load feature time sequence signal, decomposing the signal into a series of intrinsic mode function components, and carrying out the VMD-LSTM. An improved wavelet packet soft threshold function is used, a lugish-greed mechanism is provided to improve the function, denoising processing is performed on IMF component signals, feature coefficients and modal kurtosis values of all components are extracted, and the denoising effect is evaluated; a network structure of a long short-term neural network is improved, a parameter adaptive adjustment optimization algorithm is provided, the learning rate of the LSTM is dynamically optimized, and data is input into the network for training and prediction. The accuracy of short-term load prediction and the precision of classification can be improved, and short-term accurate prediction of the power load is facilitated.

The invention relates to the technical field of cooperative control, in particular to a multi-machine cooperative control method and system for a bridge cable hoisting device, and the method comprises the following steps: collecting tension change characteristics through a distributed sensor array, extracting indexes through a sliding window algorithm to generate a response time window, and generating a cooperative time window through time sequence overlapping and speed trend adjustment. Calculating a compensation value by combining inertial parameters and acceleration to generate a trigger window, detecting tension and displacement difference, inputting the tension and displacement difference into a dynamic threshold function for screening and fusion, outputting a synchronous trigger condition, and generating a synchronous control instruction by encoding a device number and a tension change rate after timestamp alignment. According to the method, multi-node tension synchronous acquisition and normalization processing, dynamic response generation through a sliding window, boundary matching action triggering adjustment through an overlapping algorithm, response lag correction through inertia compensation, tension and displacement difference real-time detection, dynamic threshold screening and triggering condition fusion and timestamp alignment packaging instruction consistency are carried out; and the cooperation precision and the system robustness are improved.

The invention discloses a data risk management system and method based on a large model, and relates to the technical field of data risk identification, and the data risk management method specifically comprises the following steps: obtaining data; the obtained data is preprocessed; analyzing data by using a large model, constructing a data relation graph, and identifying a potential hidden relation; performing association rule mining to obtain semantic association among the data; obtaining time sequence dependence among the data through dynamic time warping; carrying out correlation analysis through multi-modal fusion by taking the hidden relationship, the semantic association and the time sequence dependence as references; introducing a sensitivity dynamic grading and confidence correction mechanism to fit a risk assessment function; constructing a dynamic threshold function according to time, space and confidence feedback of the large model as elements; and providing coping suggestions according to the risk assessment result. According to the method, the hidden association between the data is identified, semantic analysis and time sequence dependency are combined, a risk assessment function is fitted, and the risk identification accuracy is improved.

The invention discloses a laboratory detection data processing method and system based on a machine learning technology, and relates to the technical field of data processing. According to the method, the signal is decomposed through discrete wavelet transform, the noise standard deviation is calculated based on the median of the highest frequency coefficient, the high signal-to-noise ratio data is reconstructed through the dynamic threshold and the soft threshold function, and the signal quality is improved; a sliding window is used for extracting time sequence signal statistics and FFT frequency domain features, spatial features are extracted in combination with an SIFT algorithm, high-correlation features are reserved through mutual information screening, and redundancy is reduced; constructing a graph convolutional network anomaly detection model and an XGBoost-LightGBM weighted regression model, and eliminating pollution data through an anomaly probability threshold to obtain a normal regression predicted value; predicting a compensation amount according to the environmental parameters by using an LSTM network, and obtaining calibration laboratory data according to the normal regression predicted value and the predicted compensation amount; according to the method, the problems of noise sensitivity, feature splitting, model isolation and environment drifting of multi-source data are solved, and the laboratory analysis precision and robustness are remarkably improved.

The invention provides an under-mine video stream dynamic denoising method based on multi-modal fusion, which comprises the following steps: constructing a time sequence synchronous fusion mechanism of visible light, infrared and laser radar data, and realizing time-space alignment of multi-source heterogeneous data; a dynamic noise model is established by introducing a fractional calculus optical flow field concept and combining a Gaussian mixture model, so that a dynamic noise region is accurately identified; an improved self-adaptive wavelet threshold function is constructed, a function threshold parameter can be linked with a dust concentration sensor in real time, and the de-noising intensity is dynamically adjusted according to the actual dust concentration; designing a dual-path feature enhancement neural network to effectively separate and enhance structural features and texture features in the video image; a cascaded detection decision system is created, a lightweight network is used as a primary detector, a high-confidence detection result is directly output, and a low-confidence detection result is input into a Transform correction module for secondary reasoning. According to the invention, dynamic denoising, feature enhancement and target intelligent monitoring of the video stream under the mine can be realized.

The invention discloses a distributed ground well data processing method, and relates to the technical field of data processing, and the method comprises the steps: collecting the vibration data of an air conditioning unit of an airport distributed ground well through an acceleration sensor; de-noising processing is carried out on the collected vibration data of the air conditioning unit by combining a time segmentation clustering algorithm on the basis of the wavelet decomposition layer number dynamically adjusted based on the signal-to-noise ratio and a threshold value strategy; performing segmented feature coding on the vibration data of the air conditioning unit; the deep neural network model based on feedback rewards carries out training optimization through a feedback reward mechanism, and the performance of the model in a complex fault diagnosis task is improved; and performing fault diagnosis classification on the vibration data of the air conditioning unit by using the trained deep neural network model based on the feedback rewards. According to the method, a signal-to-noise ratio driven wavelet decomposition layer number and threshold value control mechanism is adopted, the decomposition depth and the threshold value function are dynamically adjusted according to the signal local standard deviation, and the capability of retaining fault features under different working conditions is improved.

The invention relates to an air coupling ultrasonic signal processing method based on a COA-VMD combined improvement wavelet threshold, and the method specifically comprises the following steps: 1, employing a meta-heuristic algorithm Coati, and introducing a minimum envelope entropy as a fitness function of the algorithm, and taking the minimum envelope entropy as an evaluation criterion of an optimal solution; 2, decomposing a noisy part in the air coupling ultrasonic signal into a plurality of intrinsic mode function components by using the optimized VMD decomposition parameters; and 3, on the basis of the step 2, distinguishing an effective ultrasonic signal component f1 and a noise component f2 in the detected IMF components by adopting a correlation coefficient method. And step 4, processing the noise component f2 by adopting an improved wavelet threshold function algorithm to obtain a noise-reduced air coupling ultrasonic signal. According to the method, COA-VMD and an improved wavelet threshold noise reduction method are combined, the signal-to-noise ratio is effectively improved, and a technical basis is provided for high-precision detection of defects in the honeycomb sandwich structure.

The invention provides an industrial production Internet of Things data anomaly detection method, medium and system, and belongs to the technical field of industrial production Internet of Things. Industrial equipment sensor data is collected and preprocessed to establish a multi-dimensional data set, and principal component analysis and a mutual information algorithm are used to construct a dimension reduction feature data set; a virtual sensor algorithm is utilized to make up for data missing to form an extended data set, a simulation statistical mechanical anomaly analysis model is established based on a statistical mechanical law to convert the microscopic state of massive high-dimensional sensor data into a macro thermodynamic parameter, and a statistical mechanical feature vector is calculated through a virtual particle ensemble simulation equipment operation state. A state evaluation model and a dynamic threshold function are adopted to identify an abnormal mode and perform grading marking, a feedback optimization mechanism is constructed to continuously improve the system performance, and the technical problem that a traditional algorithm has a curse of dimensionality and cannot perform effective anomaly detection due to extremely high data dimensionality of an industrial equipment sensor is solved.

The invention relates to the technical field of radar electronic countermeasures, and particularly discloses a radar radiation source open set identification method (ARPLAD) based on countermeasure reciprocity point learning. The method comprises the steps that adaptive noise reduction and feature extraction are conducted on radar signals through a feature extraction network fusing a DRSN module and an ECA module, channel-level adaptive noise reduction is achieved through the DRSN by means of a soft threshold function, and key features are strengthened through dynamic weight distribution by means of the ECA; an adversarial reciprocity point learning framework is adopted, reciprocity points are set for each known class to serve as out-of-class space representation, and known and unknown class feature spaces are separated by maximizing the distance between known class samples and the corresponding reciprocity points; introducing a central loss function to compress intra-class feature distribution, and constructing a weighted total loss function optimization network in combination with an open space limitation function; and calculating a class self-adaptive threshold value based on the training sample, and comparing the maximum distance from the sample to each reciprocity point with the threshold value during testing to judge known and unknown classes.

The invention discloses a turning signal noise reduction method based on an improved wavelet threshold, and the method comprises the steps: carrying out the multi-layer wavelet decomposition of a noise-containing turning signal, obtaining the low-frequency and high-frequency wavelet coefficients of each layer, and processing the high-frequency wavelet coefficients of each layer through an improved threshold function, thereby achieving the noise reduction of the turning signal. And reconstructing a signal through wavelet inverse transformation of the processed wavelet coefficient to realize denoising. The optimal number of decomposition layers required by a sampling signal is determined according to noise power, and finally, an optimal wavelet basis is determined by taking a signal-to-noise ratio, a mean square error and the like as evaluation indexes. By means of the improved wavelet threshold function, on one hand, the signal denoising effect can be achieved, and on the other hand, the situation that details are damaged due to excessive noise reduction can be avoided. Experimental data show that the improved threshold function effectively suppresses random errors, the signal-to-noise ratio is improved, the root-mean-square error is reduced, and the denoising effect is good.

The invention relates to the technical field of meteorological disaster monitoring and early warning, in particular to a rainstorm early warning method and system based on a wireless network, a terminal and a storage medium, and the method comprises the steps: collecting multi-source data through distributed sensor nodes; preprocessing the multi-source data to obtain real-time data; constructing a spatial-temporal feature extraction model; generating a dynamic threshold function, performing weighted fusion on the dynamic threshold function and a preset static threshold, and outputting a graded early warning threshold; judging whether the current accumulated water depth acceleration exceeds a gradient critical value of a graded early warning threshold value or not; and if so, outputting a rainstorm early warning level. The method has the advantages that the problem that a static threshold mechanism cannot dynamically adapt to rainstorm disaster risks in a complex environment is solved, and the accuracy and practicability of the rainstorm early warning system are improved.

The invention relates to a method for predicting the bearing capacity of a concrete-filled steel tube yielding lagging jack, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring stress data at a key stress position of a concrete filled steel tube arch solid structure; stress data screening is carried out, and a screened data set is stored in an HDF5 hierarchical format; denoising the stored stress data by adopting a self-adaptive normalization method and a wavelet threshold function; time domain-frequency domain feature fusion is carried out on the stress data after denoising normalization through dynamic time segmentation and short-time Fourier transform; constructing a concrete-filled steel tube yielding arch bearing capacity prediction model, inputting the fusion feature vector into the model, and training the model to obtain a trained model; and processing the collected data, inputting the processed data into the trained model to obtain a predicted value of the bearing capacity, and triggering early warning when the predicted value exceeds 90% of the designed bearing capacity for three continuous times. The prediction capability of the model can be improved.

The invention relates to the technical field of ultrasonic signal processing, in particular to a wavelet and MAD adaptive threshold combined laser ultrasonic signal denoising method. The method comprises the following steps: firstly, preprocessing an acquired trigger channel signal and an ultrasonic channel signal, determining a signal starting point through differential positioning, and intercepting an effective signal segment; carrying out multilayer wavelet decomposition on the effective signal segment to obtain a wavelet coefficient of each layer; extracting a detail coefficient of the highest decomposition layer, and adaptively estimating a noise standard deviation based on a median absolute deviation criterion; calculating an adaptive threshold according to the noise standard deviation and the signal length, and processing each layer of wavelet coefficient by adopting a hard threshold function; and finally, carrying out wavelet inverse transformation reconstruction to obtain a denoised signal. According to the method, prior noise information is not needed, the noise level can be adaptively estimated, the optimal threshold value can be determined, the signal features are reserved while noise is effectively suppressed, and the signal-to-noise ratio is remarkably improved.

The invention discloses a coarse-to-fine point cloud registration method, and the method comprises the steps: carrying out the preprocessing of a to-be-registered source point cloud and a to-be-registered target point cloud, and obtaining a first point cloud and a second point cloud after preprocessing; performing coarse registration on the first point cloud and the second point to obtain an initial matching coefficient; performing iterative solution on the target function and the threshold function based on the initial matching coefficient, the source point cloud and the target point cloud until an optimal matching coefficient meeting a convergence threshold condition is obtained; and carrying out rotation and translation operation on the source point cloud based on the optimal matching coefficient, converting the source point cloud into a coordinate system where the target point cloud is located, enabling the source point cloud and the target point cloud to be aligned in the same coordinate system, and completing registration operation. According to the method, the point cloud is preprocessed and coarsely registered, and then the optimal matching coefficient is solved step by step through iteration, so that the defects of a traditional method can be effectively overcome, and the point cloud registration efficiency and precision are improved.

The invention relates to the cross technical field of geological disaster monitoring and signal processing, and provides a GNSS tunnel portal slope deformation time sequence noise reduction method, system and medium combining CEEMD and an adaptive wavelet packet threshold function, the method integrates the advantages of CEEMD and wavelet packet adaptation, processing of mixed entropy demarcation, adaptive threshold, dynamic smoothing and multi-path compensation is used, and the noise reduction efficiency is improved. Useful deformation features can be reserved while more comprehensive and finer noise reduction is performed, smooth compression can be performed in a large signal part through improved threshold function processing, excessive suppression can be avoided, smooth transition is performed in a small signal part through exponential attenuation, features of the large signal part are reserved, noise suppression is realized, signal details are reserved to the greatest extent, and the noise suppression effect is improved. The noise reduction requirement of tunnel portal side slope monitoring data is met, and particularly in side slope monitoring of a tunnel portal in a complex environment, the precision and reliability of GNSS deformation monitoring are improved.

The invention relates to the technical field of marketing operation based on big data analysis, and discloses a marketing operation method based on big data analysis. Preprocessing the user-commodity interaction data to construct an initial interaction matrix; constructing a co-occurrence counting matrix; automatically calculating a sparse penalty coefficient and a convergence threshold; setting an optimization objective function and a soft threshold function; initializing a projection matrix and iteratively solving a sparse projection matrix through a coordinate descent algorithm; constructing a commodity sparse co-occurrence adjacency set; and generating a personalized recommendation result in combination with user historical behaviors. The analysis quality is improved through data preprocessing and denoising, commodity potential correlation is mined through co-occurrence modeling, adaptability is enhanced through automatic parameter adjustment, a projection matrix optimization result is sparse and interpretable, the structure sensing capacity of recommendation is improved through an adjacent structure, and finally high-precision and high-correlation recommendation is achieved. The structure definition, the calculation efficiency and the model generalization ability are excellent, and an efficient and stable personalized marketing recommendation system is constructed.

The invention relates to a switch cabinet partial discharge signal positioning method based on a time difference positioning method, and belongs to the technical field of electrical engineering and signal processing. Aiming at the problem that the ultrasonic partial discharge signal has white noise, an improved wavelet threshold algorithm is adopted to construct a threshold function, and a continuous self-adaptive wavelet threshold denoising algorithm is adopted to carry out denoising processing on the partial discharge signal, so that the problems of excessive denoising and incomplete denoising existing in a traditional denoising algorithm are solved, effective separation of the signal and the noise is realized, and the denoising efficiency is improved. And the performance of the denoising algorithm is improved. In order to obtain a time delay value between each path of signals, a generalized cross-correlation time delay estimation algorithm based on PHAT-SCOT joint weighting is used for substituting a time delay estimation value into a switch cabinet partial discharge positioning equation set, and an optimal equation is obtained. According to the partial discharge optimal solution algorithm based on DE-PSO, the optimization equation is solved, the spatial coordinate position generating the partial discharge signal is positioned, the defects of parameter setting and threshold selection of the optimization algorithm are overcome, and the positioning precision of the partial discharge signal is improved.

The invention relates to a transformer fault identification method based on voiceprint signals, and belongs to the technical field of cepstrum for extracting parameters in audio decoding or coding. The method comprises the following steps: setting a fault type and establishing a fault identification model for training; arranging an acoustic sensor to collect voiceprint signals of the transformer; utilizing a dream optimization algorithm to optimize the penalty factor and a successive variational mode decomposition method to decompose a plurality of mode components, and dividing the mode components into pure components and noisy components; noise reduction is carried out on the noisy component by adopting a designed threshold function in combination with wavelet threshold noise reduction, and the noisy component and the pure component after noise reduction are input into the recognition model to obtain a probability vector; and finally, fusing into a first fusion probability vector and a second fusion probability vector through fuzzy measurement, and taking the fault type corresponding to the maximum second fusion probability as the fault type of the transformer. The method can accurately capture the mapping relation between the acoustic features and the transformer fault state, and accurately identifies the transformer fault type.

The invention provides a heterogeneous multi-agent system control method under a DoS attack based on an event triggering strategy. Robust cooperative control under a complex attack environment is realized through the following steps: S1, performing state description and topological structure modeling on a heterogeneous multi-agent system; s2, carrying out approximation on an unknown nonlinear function in the system; s3, restraining the error dynamic state through a performance function, and ensuring that the system response meets the preset precision; s4, designing a time-varying threshold function, and dynamically adjusting a trigger condition to reduce communication burden; s5, designing an anti-attack controller in combination with event triggering and a Lyapunov theory; s6, designing an adaptive law to carry out online estimation on disturbance; and S7, testing synchronization precision, communication efficiency and anti-attack performance based on a physical platform. According to the method, deep fusion of the event triggering mechanism and the preset performance constraint is realized for the first time, and the self-adaptive dynamic threshold strategy is introduced, so that malicious interference of DoS attacks on a communication link is effectively dealt with, and it is ensured that the heterogeneous nodes can still meet preset performance indexes in an attack scene.

The invention provides a wavelet-CNN satellite communication interference signal detection method and system based on data driving, and belongs to the field of satellite communication signal processing. The problems that an existing signal detection technology is high in algorithm complexity and low in interference signal detection accuracy are solved. According to the method, the ground radiation source communication signal received by the satellite is de-noised by fusing the soft and hard threshold functions to improve the wavelet threshold function, so that noise and interference can be effectively suppressed, and meanwhile, key detail information of the signal is reserved; two-dimensional time-frequency data are constructed through Fourier transform and normalized, a convolutional neural network is trained to enhance the expression ability of signal features, and finally a signal detection result is tested and output. According to the method, the reliability of signal processing can be improved, the overall performance of a satellite-borne communication system can be optimized, and an effective solution is provided for signal monitoring in a complex channel and a dynamic environment.

The invention discloses an ocean electromagnetic signal denoising method based on wavelet transform, which comprises the following steps of: performing wavelet decomposition on a noisy ocean electromagnetic signal to obtain a wavelet coefficient of each layer; carrying out threshold calculation on the high-frequency wavelet coefficient and obtaining soft and hard threshold functions for processing the wavelet coefficient; and performing self-adaptive threshold processing on the high-frequency wavelet coefficient, namely quantifying the high-frequency wavelet coefficient by adopting a self-adaptive threshold function which dynamically adjusts the characteristics of a soft threshold and a hard threshold by adjusting a parameter k belonging to [0, 1], and when k is equal to 0, the self-adaptive threshold function is equivalent to the soft threshold function, when k is equal to 1, the self-adaptive threshold function is equivalent to the hard threshold function, and when k is equal to 0, the self-adaptive threshold function is equivalent to the hard threshold function. To eliminate discontinuity and constant deviation at the threshold; 4, performing inverse wavelet transform on the processed wavelet coefficient, and reconstructing to obtain a de-noised ocean electromagnetic signal; not only are the signal-to-noise ratio and definition of the signals significantly improved, but also a more reliable and accurate data basis is provided for real-time monitoring, accurate analysis and subsequent scientific research of the marine electromagnetic signals.

The invention discloses a client-side risk analysis method based on voice recognition, and the method comprises the steps: carrying out the mixed sampling through obtaining the receiving data, voice interaction background information and voice signals of an equipment-side sensor, and generating a multi-dimensional feature vector; based on a support vector machine model, classifying the multi-dimensional feature vectors by taking historical scene data as training data to obtain scene categories and scene confidence; meanwhile, acoustic characteristics and historical voice behaviors of the voice signals are extracted, voice risk factors are constructed, a total risk score is comprehensively calculated, and risk score judgment is carried out through a dynamic threshold function; according to a judgment result, selecting to check a voice risk factor or regenerate a multi-dimensional feature vector so as to realize accurate analysis of the customer side risk; through feature fusion and classification optimization, the accuracy and real-time performance of risk analysis are effectively improved, and the method is suitable for customer risk management in a complex voice interaction scene.

The invention discloses a data processing method based on an ultrahigh frequency original signal and a storage medium, and relates to the field of data processing, and the method comprises the steps: carrying out the adaptive gain adjustment collection of the ultrahigh frequency original signal, and enabling the signal amplitude to be stabilized in a preset effective interval through the dynamic adjustment of a gain parameter, acquiring initial acquisition data containing complete discharge characteristics; performing time-frequency domain combined noise reduction preprocessing on the initially acquired data, constructing an adaptive threshold function based on signal frequency domain distribution characteristics, and filtering background noise and non-discharge interference signal components; according to the method, the signal amplitude is dynamically stabilized through adaptive gain adjustment, it is ensured that initial collection data completely contains discharge characteristics, the problem of information loss caused by signal fluctuation is solved, the adaptive threshold is constructed based on frequency domain energy characteristics through time-frequency domain combined noise reduction, background noise and interference components are accurately filtered out, and the signal purity is improved.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

The invention relates to the field of deep learning, and provides a container ship loading and unloading operation time prediction method and system for solving the problems that an existing container ship operation time estimation method is rough, and operation data has noise in a real loading and unloading operation scene. On the basis of a deep residual shrinkage network DRSN-CW (Deep Residual Shrinkage Net) model with feature extraction and denoising capabilities under a ResNet-18 architecture, a fixed soft threshold function is changed into a more flexible adaptive threshold function, so that the noise reduction capability is more flexible while the feature learning capability is kept. According to the scheme of the application, the request of real-time prediction of the quay crane operation duration in the berth planning process can be well met with relatively high average detection precision and robustness.

The invention belongs to the technical field of spacecraft navigation, and provides a pulsar TOA extraction method based on periodic coincidence optimal estimation. The method comprises five steps of dual-channel detection photon simulation, periodic coincidence analysis, coincidence time window optimization, TOA sequence generation and optimal TOA extraction analysis. The method comprises the following steps: constructing a threshold function by utilizing the periodic characteristics of pulsar signal photons, periodically sliding a time window to carry out coincidence processing, eliminating noise photons, and reserving the signal photons. And optimizing the coincidence time window to improve the noise rejection ratio to more than 90%, generating a TOA measurement sequence, and selecting the minimum TOA measurement value as the optimal estimation from the TOA measurement sequence. According to the method, the pulsar TOA measurement precision can be effectively improved, and especially in a short time and under a small detector area, the method has important significance for an X-ray pulsar navigation system to realize high-precision spacecraft autonomous navigation and positioning.

The invention relates to the technical field of geographic information systems, and discloses a geographic information data processing method and system.The geographic information data processing method comprises the steps that geographic data are packaged into self-activation data nodes; monitoring a network state in real time through a network environment perception function; constructing an activation threshold function based on a neuron activation principle to determine data transmission; carrying out multi-scale analysis on the data stream by applying wavelet transform to realize self-adaptive compression; predicting hotspot distribution dynamic scheduling resources through space-time analysis; an edge-fog-cloud three-level cooperative processing architecture is constructed to realize distributed processing; according to the method, the technical problem of geographic information data transmission and processing in an unstable network environment is solved, the overall performance and the adaptive capacity of the system are improved through a multi-level adaptive mechanism, and the method has wide application value.

The present invention discloses a data processing method and system for infrared thermal imaging. The method includes obtaining infrared thermal imaging information; performing a noise reduction operation on the infrared thermal imaging information to obtain denoised wavelet coefficient values; constructing a threshold function based on the denoised wavelet coefficient values to obtain a local weight threshold function and a high threshold function; performing an image reconstruction operation based on the denoised wavelet coefficient values, the local weight threshold function, and the high threshold function to obtain reconstructed image data; performing a first image enhancement operation on the reconstructed image data to obtain a first enhanced image; performing a second image enhancement operation on the first enhanced image to obtain a second enhanced image; and performing an image output operation on the second enhanced image to obtain an output image. This method can achieve maintaining image details and smoothing the noise.

The invention discloses a power distribution network reliability intelligent analysis method and analysis system, particularly relates to the technical field of power system fault detection, and is used for solving the problems of misjudgment and missed judgment caused by fault feature weakening and harmonic interference in a bidirectional power flow scene in an existing method. The method comprises the following steps: firstly, synchronously collecting fundamental wave and harmonic components of nodes of a power distribution network, extracting a current amplitude change rate and a phase offset by adopting time-frequency domain conjoint analysis, and generating dynamic distribution characteristics by combining harmonic energy clustering; identifying high-frequency interference components in a bidirectional power flow mode by quantizing a phase deviation direction and harmonic energy aggregation characteristics; based on a harmonic frequency band coupling effect and a current dynamic association relationship, an adaptive detection threshold function is constructed, and accurate separation of fault features is realized; and finally, combining a real-time fault judgment result with historical repair data to generate a reliability evaluation parameter. And the sensitivity of fault detection and the rationality of reliability evaluation in a complex operation environment are remarkably improved.