1396 results about "Noise level" patented technology

The local intrinsic dimensionality (LID) for a diffusion model with respect to a particular data sample is determined by using the diffusion model's diffusion process to apply noise to a data sample and evaluate how the estimated log probability of the data sample changes at different levels of noise. Particularly, the differential of change in noise to change in log probability can be used to determine the local intrinsic dimensionality. This may be determined by evaluating the log probability at several noise levels and determining a slope of the difference. In additional examples, the differential is evaluated directly at a selected noise level. The selected noise level can be optimized by calculating the estimated LID for various data samples at a variety of noise levels and selecting the LID that corresponds to a “knee” where the estimated LID sharply changes.

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention relates to the technical field of unmanned aerial vehicle-unmanned ship data fusion, and discloses an unmanned aerial vehicle-unmanned ship multi-source heterogeneous big data fusion system and method, and the system comprises a task scheduling module which is used for executing the path planning and task distribution of operation tasks; the control and data acquisition module is used for controlling the unmanned aerial vehicle and the unmanned ship to execute a synchronous measurement task; the space-time reference unification module is used for performing dynamic synchronization and consistency alignment processing; the multi-source data fusion module is used for carrying out fusion processing on the aligned heterogeneous sensing information data; the self-adaptive robust enhancement module is used for monitoring the noise level and the change condition of the signal-to-noise ratio of the sensor in the fusion process; and the intelligent analysis and visualization module is used for executing environment modeling, target identification and geographic information expression processing. According to the method, the fusion weight self-adjusting mechanism driven by the maximum entropy is introduced, so that the internal structure can be intelligently adjusted when the data quality fluctuates. According to the technical effect, the target that the weight is dynamically adjusted along with signal noise is achieved.

The invention relates to the technical field of data processing, in particular to an anti-interference six-dimensional sensor acquisition system, which comprises a multi-modal signal fusion module for receiving original acquisition data of a six-dimensional sensor and realizing fusion of the acquisition data of all sensors based on a self-adaptive dynamic weight distribution algorithm; the intelligent error compensation module is used for performing forward compensation on errors in the six-dimensional sensor data so as to improve the accuracy of the data; the correction feedback module is used for carrying out self-correction on the data acquired by the six-dimensional sensor at an application end and carrying out feedback adjustment on sampling parameters according to a correction result; according to the method, a self-adaptive covariance matched filtering algorithm is adopted, a noise covariance matrix is dynamically adjusted and measured in combination with Kalman filtering, the denoising effect is improved, and the optimal signal quality is ensured to be kept under different noise levels by setting a dynamic smoothing factor and adaptively adjusting a data smoothing process.

The invention relates to the technical field of network management, in particular to an Internet of Things gateway control method and system based on AI computing power, and the method comprises the following steps: obtaining channel real-time signal intensity, noise level and occupancy proportion, monitoring protocol type and transmission condition, analyzing the relationship between retransmission times and interference, deducing protocol combination interference and throughput, and obtaining a real-time channel signal intensity, noise level and occupancy proportion. And configuring an optimal scheduling scheme, evaluating resource consumption and load states, packaging and requesting permission entrustment, and judging permission information in combination with an interaction record and an access control list. According to the method, different protocol distribution combinations are simulated and deduced, the data throughput of the whole network is further estimated and optimized, potential protocol conflicts are reduced, the use efficiency and the overall performance of network resources are effectively improved, consumption of a local computing unit and network bandwidth is comprehensively considered, packaging processing of request information is optimized, and the network performance is improved. And the real-time response capability and manageability of the network state are obviously improved.

The invention belongs to the technical field of leakage detection, and particularly relates to a leakage detection method based on multi-sensor time shift correction and deep learning. A plurality of sensors sensitive to leakage sound waves or vibration are arranged at different positions of a monitored pipeline or container and used for collecting leakage related signals; time alignment is carried out on the time sequence signals collected by the multiple sensors according to the position difference of the time sequence signals in the space; comparing and differentiating the plurality of sensor signals subjected to time alignment; modeling and updating environmental noise in real time, dynamically adjusting detection parameters according to noise level, continuously monitoring statistical characteristics of background signals, and establishing a noise model; a pre-trained deep learning model is used to carry out mode identification and classification determination on the signal after noise reduction processing; and the alarm and display module is used for triggering alarm and recording leakage information when suspected leakage is detected. According to the invention, weak leakage signals can be reliably detected under the condition of strong background noise.

A system using global and local-aware denoising framework for commonsense knowledge graph noise detection is provided. The system includes an input receiver, a triple encoder, a global noise detector, a local noise detector, and a comprehensive evaluation scorer. The triple encoder receives the CSKG information from the input receiver and extract semantic information from free-text formatted nodes in the CSKG information, so as to obtain structured triples. The global noise detector identifies high-frequency patterns through rule mining and uses a rule encoder to generalize learned rules and guide noise detection. The local noise detector utilizes the structured triples output by the triple encoder to aggregate neighboring triple information around both the head and tail nodes via graph networks. The comprehensive evaluation scorer provides a comprehensive score based on global noise detection and local noise detection, so as to indicate a noise level for the CSKG information.

The invention relates to the technical field of CT scanning parameter optimization, in particular to a CT scanning parameter optimization method and system based on reinforcement learning, and the method comprises the steps: obtaining basic information of a patient, scanning part information and initial CT scanning parameters; obtaining a CT image quality evaluation data set; constructing a reinforcement learning environment based on the basic information of the patient, the scanning part information and the initial CT scanning parameters; using the CT image quality evaluation data set to train a reinforcement learning model; according to the reinforcement learning model, optimized CT scanning parameters are generated; according to the method, the optimized CT scanning parameters are output and used for executing CT scanning, the system can generate customized optimal scanning parameters for each patient by taking the individual features of the patient as a part of a reinforcement learning environment, the accuracy and safety of CT examination are greatly improved, multi-target collaborative optimization is achieved, and the method is suitable for popularization and application. The optimal balance point can be found among a plurality of targets such as image quality, noise level and radiation dose.

A system and method for real-time, identity-free personalization using deformable emotional trait vectors to dynamically adapt digital and voice-based experiences. Each user session is modeled as a behavioral object known as a Vectra, composed of fluidic traits—such as mass, viscosity, temperature, volatility, and texture—that evolve continuously in response to live behavioral, contextual, environmental, and voice-derived signals. These Vectras traverse a dynamically warped emotional space, the Vectraverse, influenced by ambient conditions including time of day, noise level, inventory urgency, and engagement rhythm. Gravitational pull toward predefined emotional goal attractors modulates system behavior, while a goal mutation engine reclassifies session intent when confidence decays or friction spikes. Outputs include tone modulation, content pacing, offer framing, and gamified reward logic—all executed without storing identity, login credentials, or historical profiles. The system supports modular deployment across voice, screen, signage, mobile, and in-room environments, and integrates with large language models, AI agents, and third-party personalization stacks via privacy-safe APIs and federated learning. Designed for zero-ID personalization, the platform enables emotionally intelligent, context-aware engagement across any surface or session.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention discloses a machine learning method and system based on privacy protection enhancement, and relates to the technical field of data processing. The method comprises the steps of monitoring real-time data streams of a plurality of data sources based on real-time input data, analyzing relationships among various data, drawing a data association network diagram, evaluating information leakage risks and privacy sensitivity of a plurality of data points by analyzing interconnection density and path distribution of nodes in the network diagram, and generating sensitivity evaluation indexes. According to the method, the security and privacy protection level in the data processing process are improved by identifying the risk points in the data in real time and specifically adjusting the noise level of the data points, and the encryption processing efficiency and security are improved by comprehensively considering the data processing efficiency, security level requirements and computing resource consumption; the stability of encrypted data is tested by simulating network attack conditions, the capability of coping with network attacks is enhanced, input data is enabled to support various data analysis tasks, and the privacy and security of the data are ensured.

The invention relates to the technical field of myopia prevention and control, and discloses a remotely adjustable child safety distance television control system, which comprises a multi-sensor fusion module used for detecting the distance between a child and a television in real time, and cutting off a television power supply when detecting that the distance between the child and the television is smaller than a set early warning distance; the posture recognition module indicates the child to adjust the sitting posture through voice when the posture does not meet the health standard; the eye fatigue detection module is used for judging the fatigue degree by detecting the blinking frequency and the eye closing duration of the child; the environment sensing module is used for detecting indoor light intensity and noise level; and the loudspeaking module is used for sending a prompt and an instruction to the child through voice. The watching distance, the sitting posture and the eye using state of a child are monitored in real time through the multi-sensor fusion module, the posture recognition module and the eye fatigue detection module, intervention is conducted in time, asthenopia and myopia caused by short-distance watching, poor postures and long-time eye using are avoided, and the eyesight health of the child is effectively protected.

The invention relates to the technical field of image data processing, in particular to a connector surface defect detection method and system based on image processing, and the method comprises the steps: obtaining a connector surface image; the method comprises the following steps: calculating a first noise factor of each pixel point in a connector surface image to quantify the noise level in the image, calculating a second noise factor based on the first noise factor to more accurately evaluate the noise feature of the image, and then partitioning the connector surface image to obtain a plurality of blocks with the same size, and calculating the noise expression degree of each block based on the second noise factor, removing the blocks of which the noise expression degrees are greater than a set expression threshold value, calculating fuzzy entropy values of the other blocks, and if the fuzzy entropy values are greater than the set threshold value, judging that the surface of the connector has defects. The problem that the accuracy of connector surface defect detection is not high is solved.

The invention discloses a pattern recognition type metal pipeline anticorrosive coating quality detection device and system, belongs to the technical field of sensing monitoring, and aims to solve the problems of low detection precision and difficulty in positioning of damaged points of a metal pipeline anticorrosive coating under the interference of a complex environment. The method comprises the following steps: generating an environmental interference spectrum feature library through full-range frequency band pre-scanning, dynamically establishing a reference noise level, screening candidate frequencies of detection signals, and switching the frequencies as required to guarantee signal quality; synchronously acquiring a potential gradient and an electromagnetic radiation signal, and marking space-time coordinates; performing deep processing on the acquired signals, separating multi-frequency signals, correcting attenuation, and constructing a multi-dimensional feature vector; based on the feature vector, a space-time abnormal mode is extracted to position a damage point, through damage type classification, region area measurement and depth evaluation, the damage severity is quantified, accurate data support is provided for pipeline maintenance, and the accuracy and reliability of metal pipeline anticorrosive coating detection are effectively improved.

The invention provides a self-adaptive bearing fault diagnosis method based on multi-base wavelet fusion. The objective of the invention is to solve the problems of noise reduction, insufficient feature extraction and low diagnosis precision under noise conditions. A Kaisixi University bearing public data set is used as original data, and Gaussian noise with different SNRs is superposed to simulate various noise intensities. And uniformly carrying out length alignment, down-sampling, equal-length segmentation, division and normalization preprocessing. Then, wavelet bases such as sym4, db4, coif5 and the like are adopted for parallel multi-scale decomposition and reconstruction; and adaptively determining the number of decomposition layers and a threshold strategy according to the noise level, and generating a de-noising branch. And performing weighted fusion on the denoising results of the branches, and performing iterative denoising on the residual error. Signals subjected to noise reduction processing are sent to a double-branch convolution-cycle-attention network, a convolution layer extracts features, an LSTM and a self-attention module capture time sequence changes, and accurate recognition of various bearing faults is achieved. The training adopts a segmented attenuation learning rate and an early stop strategy, and the robustness and generalization ability of different SNR working conditions are improved.

The invention relates to a bridge modal parameter automatic identification method and system considering multi-channel information, and the method comprises the following steps: S1, directly analyzing a multi-channel monitoring signal through COV-SSI, generating a stability diagram, and automatically extracting a stability axis in combination with a DBSCAN clustering algorithm, thereby achieving the automatic identification of a modal frequency; s2, performing signal decomposition on the multi-channel monitoring data by adopting MvFIF to generate an intrinsic mode function (IMF) group with a mode alignment characteristic; s3, the instantaneous frequency and bandwidth of the IMF in the step S2 are calculated through HHT, IMF components containing target modal frequency are screened out, and linear reconstruction is carried out; s4, taking the multi-channel monitoring data reconstructed in the step S3 as system input of a COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating a modal damping ratio by utilizing eigenvalue decomposition; according to the method, the spatial-temporal correlation among multi-channel data can be considered, and synchronous decomposition of multi-channel monitoring data is realized; and automatic and accurate identification of the structural modal parameters under different noise levels can be realized.

The invention relates to a navigation equipment abnormity monitoring method based on an inverted attention mechanism and a diffusion model. The method comprises the following steps: acquiring time sequence data related to navigation equipment from a navigation system; an inverted attention mechanism is adopted, and a Transform model is improved; training an improved Transform model based on a diffusion model, and in a diffusion stage, adopting an adaptive noise injection mechanism, dynamically adjusting a noise level, and selecting noise intensity to gradually add noise to multivariate time sequence data; in the denoising stage, using noisy navigation system time sequence sample data to train an improved Transform model; and using an improved Transform model to obtain predicted time sequence data according to the real-time navigation system data, comparing the predicted time sequence data with the healthy time sequence data, and detecting and identifying abnormity. According to the method, an inverted attention mechanism is adopted, and the defects of traditional Transform in multivariate time sequence coding are overcome; data distribution is learned through the diffusion-denoising process of the diffusion model, and the accuracy and prediction precision of abnormal detection of the navigation equipment are improved.

The invention relates to the technical field of Internet of Things communication, and provides a low-power-consumption semiconductor communication method based on Internet of Things, which comprises the following steps: acquiring temperature, electromagnetic interference intensity and signal attenuation coefficient data through a multi-mode sensor node, and generating an original environment data set; performing dynamic noise classification on the original environment data set, dividing noise levels and marking noise types; performing hierarchical processing on the data by adopting a multi-stage self-adaptive noise reduction algorithm to generate a preprocessed data set; extracting dynamic characteristic parameters of a communication channel, constructing an energy efficiency prediction model, and dynamically calculating a combination of optimal transmitting frequency, power and modulation order; generating a multi-system modulation signal, superposing a dynamic coding strategy, and adjusting the symbol block length and the error correction code density; and the receiving end analyzes the signal quality index, feeds back the signal quality index to the transmitting end to optimize the parameters and updates the energy efficiency prediction model. According to the invention, the energy efficiency ratio and the anti-interference capability of the Internet of Things communication system can be improved.

The invention discloses a metal resource assessment method and system based on geological exploration, relates to the technical field of mineral data processing, and effectively solves key problems in geological resource assessment through multi-module collaboration. A data fusion module integrates drilling data Zk, geophysical data Dw and remote sensing data Yg; the confidence Rx is calculated by using an adaptive weighting algorithm and probability filling is carried out, so that the data sparsity error is remarkably reduced; the boundary optimization module is used for calculating a mineralization continuity index Lx and generating a boundary optimization coefficient Ly in combination with the temperature dynamic factor Tx so as to realize intelligent correction of the boundary of the ore body; the depth correction module constructs a depth compensation model through the signal intensity M, the stability V and the noise level N, the deep data credibility Sx is improved, Monte Carlo simulation and dynamic parameter adjustment are matched, the system overcomes the defects of a traditional method in the aspects of data uncertainty, boundary ambiguity and deep precision, and the method has the advantages of being high in accuracy and high in reliability. And full-process automation and high-precision resource evaluation and early warning are realized.

The invention relates to a cooling noise control method and device for a sand-proof cooling fan in a high-temperature environment. The method comprises the following steps: measuring and analyzing fan blade parameters, a dustproof net cover structure and temperature-dust environment data, and establishing a high-temperature dust coupling dynamic model considering dust adhesion and temperature deformation; performing cyclostationary characteristic decomposition on the fan operation acoustic signal to obtain an environment sensitive Gaussian mixture cyclostationary noise model; calculating a cyclic spectrum correlation function for the fan noise signal and extracting a feature vector to obtain a noise source identification result under a high-temperature sand and dust working condition; and constructing a heat dissipation efficiency function and a noise level function in a temperature-dust-rotating speed three-dimensional parameter space, executing weight adaptive adjustment calculation, and outputting an optimal rotating speed control strategy. Dynamic balance between heat dissipation requirements and noise control is achieved, and the noise level is effectively reduced on the premise that the heat dissipation efficiency is guaranteed.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention relates to a place recommendation method MSDRec based on graph masks and space-time diffusion, which comprises two core modules, the first module is a global trajectory flow graph mask self-supervision module, and the module constructs a global trajectory flow graph and carries out a graph mask self-supervision learning method suitable for POI recommendation. The strategy focuses on the core structure information of the reserved graph and deeply recovers the key edge, so that the noise caused by structural damage can be effectively reduced, and the accuracy and robustness of the model during data sparsity processing are improved. And the second module is a space-time diffusion enhancement module which encodes the space-time relation characteristics into low-dimensional embedding by constructing a global space-time transfer graph, and further de-noising and refining are carried out through a diffusion model. The diffusion process is performed within the representation space, including multi-step noise increase and removal. This process handles various noise levels, enhancing the ability of the model in identifying and eliminating noise in spatio-temporal data, thereby optimizing and refining spatio-temporal POI representations.

The invention relates to a sonar image generation method and device based on a diffusion model, equipment and a medium, and the method comprises the steps: carrying out the multi-time noise addition of each sonar actual measurement image through a forward process based on the diffusion model, and obtaining a plurality of noise images with different noise degrees; a plurality of noise images generated by the same image and corresponding semantic guidance graphs are used as a group of training data, the training data are input into a sonar image denoising network constructed based on a reverse process of a diffusion model to train the sonar image denoising network, in the training process, under the guidance of the semantic guidance graphs, the noise images are denoised for multiple times, and the noise images are denoised for multiple times. Learning parameters in the sonar image denoising network are adjusted according to a denoising result predicted by the network and an error of a corresponding real noise image, and finally, randomly generated noise images and semantic images are input into the trained sonar image denoising network for multiple times of sampling. And generating a sonar image related to the semantic image. By adopting the method, the high-quality sonar image can be efficiently generated.

The present invention relates to the technical field of three-dimensional noise map drawing, and specifically discloses a three-dimensional noise map calibration and drawing method based on vertical measured data, including: S1, obtaining multi-dimensional noise data and noise perception related data in the region, S2, constructing a preliminary three-dimensional noise distribution model, S3, drawing a noise perception contour map, S4, comparing the drawn noise contour map with the measured data and the perception data, identifying errors and S5, generating a risk map, and outputting a three-dimensional noise map; the present invention utilizes a geographic information system and three-dimensional visualization technology, combined with vertical measured data, to construct a preliminary three-dimensional noise distribution model, which can intuitively display the distribution of noise in three-dimensional space. Through a multimodal data fusion algorithm, the spatiotemporal weights of vertical monitoring data are introduced into the model, realizing the comprehensive embodiment of the physical noise level and spatial distribution impact, making the noise map closer to people's reality.

The invention provides a method and a system for intelligently controlling noise of a transformer outer cover, and the method comprises the steps: monitoring the operation state data of a transformer in real time through installing a plurality of sensors, carrying out the primary processing of the data on an edge calculation node, transmitting the data to a central server through a wireless sensor network, and extracting features through principal component analysis; an LSTM deep learning model is constructed based on historical data to predict a noise level, load distribution and current are dynamically adjusted according to a prediction result, an energy-saving mode is optimized, filter parameters are automatically adjusted by using an adaptive filter technology in combination with real-time monitoring data, and noise of different frequency bands is suppressed; the development of the user interface supports remote access and manual adjustment of working parameters, regularly evaluates the system performance and updates an intelligent control strategy, effectively reduces the noise of the transformer, improves the energy efficiency and the equipment reliability, and is suitable for transformer noise management in various industrial scenes.

The invention discloses a two-stage hyperspectral image band selection and target detection method, which comprises the following steps of: based on a two-stage band selection and background reconstruction network of a transformer, realizing band selection through a first-stage training transformer and realizing background reconstruction through a second-stage training transformer; a transform position coding module and a multi-head self-attention mechanism are used for learning similar features and difference features among wave bands, a full connection layer network is used as a clustering device, a structural similarity index and an Euclidean distance are added to serve as a loss function training model, wave band clustering is achieved, and finally a local variance is used for estimating the noise level of an image of each wave band. Obtaining a band training subset; the band subset is subjected to constraint energy minimization detection and then sent to a background feature extraction network composed of a transformer and a discriminator, background pixels are arranged through position coding, network learning background features are enhanced through a multi-head self-attention mechanism, an improved loss function is used for training, and therefore reconstruction of a background image is achieved. The invention provides a two-stage wave band selection and background reconstruction network based on transformer so as to realize hyperspectral target detection. The network comprises a first-stage wave band selection work and a second-stage background spectrum learning work. And difference detection is carried out on the final background reconstruction image and the original image, so that a final task is realized, and the detection precision is improved.

The invention relates to the technical field of data processing, in particular to a liquid cooling plate production parameter management method and system based on the Internet of Things, and the method comprises the steps: obtaining the sliding data of each stamping sliding block at each sampling moment in a plurality of complete stamping processes of a liquid cooling plate, so as to construct a sliding data point at each sampling moment; cOF detection is carried out on the current sliding data point, in the detection process, the average noise degree of the current sliding data point and the previous sliding data points is utilized to correct the track cost of the corresponding two sliding data points, the correction value is negatively correlated with the average noise degree, and on the basis of the corrected track cost, the current sliding data point is corrected. And obtaining an abnormal detection result of COF detection on the current sliding data point so as to adjust production parameters of the liquid cooling plate. According to the invention, the accuracy of the anomaly detection result can be improved, and the anomaly of the stamping equipment can be accurately monitored.

Systems and methods are disclosed for executing large quantum programs efficiently and securely. It breaks down programs into small, logical components, leveraging runtime analysis and AI-driven labeling. These components are then deployed across distributed quantum hardware nodes based on factors like noise levels, capabilities, and geolocation, optimized by a deep learning engine. Ownership and deployment paths are tracked using non-fungible tokens (NFTs) on a blockchain network, ensuring security and transparency. Outputs from each node are validated and aggregated based on their NFT linkage, resulting in accurate and reliable program results. This distributed quantum DevOps framework promotes scalability, performance optimization, and secure management, accelerating the development and application of quantum computing across diverse fields.

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.