119 results about "Noise sensitivity" patented technology

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 particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention relates to the technical field of artificial intelligence and data analysis, in particular to an unsupervised anomaly detection method and system based on comparative potential fusion. The method aims at solving the problems that in the prior art, an unsupervised anomaly detection method is limited in feature expression ability, sensitive in noise, insufficient in potential feature discrimination and lack of statistical interpretability in detection results. According to the method, the global potential features generated by comparison learning and the self-encoder reconstruction residual error are fused, the statistical model is combined for self-adaptive threshold judgment, the problems of insufficient feature expression and high noise sensitivity in multi-source heterogeneous time series data anomaly detection are effectively solved, and the method has the advantages that the detection precision and robustness are improved, and the dependence on labeled data is reduced.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

The invention discloses a TCN-SVM rolling bearing fault diagnosis method fusing an SE attention mechanism, and belongs to the technical field of mechanical fault intelligent diagnosis. Aiming at the problems of feature redundancy, noise sensitivity, insufficient Softmax classifier generalization and the like existing in a traditional time sequence convolutional network (TCN), the invention provides a solution for collaborative optimization of a deep network and a support vector machine. The method comprises the following steps: acquiring a vibration signal of the rolling bearing; constructing a multi-fault sample set, and processing an original signal; constructing an SE-TCN feature extraction network, capturing multi-scale time sequence features by adopting expansion causal convolution, and embedding an SE module into a residual module to realize channel adaptive weighting; and a support vector machine (SVM) classifier decision function is constructed, and fault classification is completed through the RBF kernel SVM. Experiments show that the method has high fault recognition accuracy and robustness, and the problem of confusion of composite fault features of the rolling bearing is effectively solved.

The invention relates to a thermal diffusivity high-precision inversion method based on a multi-terminal deep neural network, belongs to the technical field of infrared thermal wave nondestructive testing and material thermophysical property measurement, and particularly relates to the thermal diffusivity high-precision inversion method based on the multi-terminal deep neural network. The invention aims to solve the problems of low calculation efficiency, strong noise sensitivity and insufficient robustness in a traditional thermal diffusivity measurement method, and provides a thermal diffusivity high-precision inversion method based on a multi-end deep neural network for materials with damage defects. Specifically, the amplitude and phase of thermal waves are extracted by using a phase-locked thermal imaging technology, and stable characteristics describing the thermal diffusion process are obtained. A deep learning network taking space coordinates, excitation frequency, surface temperature information, amplitude and phase as input is constructed, and efficient and accurate measurement of the thermal diffusivity of a complex material is realized through multi-modal data fusion, an advanced direct current component removal technology and a multi-end deep neural network architecture.

The invention provides a video content enhancement method for a low-illumination environment, and the method comprises the steps: achieving the data preprocessing based on an original low-illumination video frame sequence through frame synchronization, color space conversion and local brightness analysis, generating a noise sensitivity thermodynamic diagram through multi-feature unsupervised learning, and constructing a noise perception gating mechanism through the combination of affine transformation. Dynamic modulation of the characteristic channel is realized; in the multi-scale network structure, a channel attention module is used for carrying out layer-by-layer self-adaptive adjustment on a noise sensitive area; a basic illumination image and an edge enhancement image are generated through double-branch decoding, and then weighted fusion is carried out in combination with a noise thermodynamic diagram, so that brightness balance and detail enhancement are realized; a noise smoothing regular term is introduced during end-to-end training, so that the network achieves dynamic balance between an enhancement effect and noise control.

The invention provides an unmanned aerial vehicle lightweight distributed anonymous bidirectional authentication method under the assistance of a block chain, and the method comprises the steps: firstly achieving the authority control through an intelligent contract disposed on the block chain, and guaranteeing that only an authorized entity can access secret information stored on the chain; then, a dynamic pseudonym mechanism is adopted, and the identity label and the session key of the unmanned aerial vehicle are updated in each session; besides, the physical unclonable function is combined with the fuzzy extractor, so that the inherent noise sensitivity problem of response of the physical unclonable function is effectively solved, and meanwhile, the security of the generated session key is kept. According to the invention, the problems of single-point fault and too high calculation / communication cost of a central ground station are solved, and the protocol does not have negative influence on the performance of the unmanned aerial vehicle because the unmanned aerial vehicle is equipment with limited calculation resources.

The invention discloses a thermal equipment water temperature control method based on a relaxation iteration PID algorithm, and aims to solve the problems that overshoot is easy to occur when a set value suddenly changes, and integral saturation and noise sensitivity are easy to cause by integral item accumulated errors in traditional PID control. The method comprises the following steps: calculating a current error e (t) = Tset (t)-Tmeas (t); a relaxation factor is introduced to dynamically adjust PID output increment, and the relaxation factor is adjusted according to a set value change rate in a grading manner; the accumulative control quantity is limited within the duty ratio range of 0-100%; and when the error absolute value is greater than 2 DEG C, freezing an integral term to avoid overshoot. According to the method, overshoot is effectively suppressed by setting a relaxation factor, differential terms are improved to reduce noise interference, and integral terms are managed in a partitioned manner to improve stability. Compared with a traditional PID control method, the method has the advantages that overshoot can be effectively restrained, the adjusting time is shortened, and the method is suitable for an industrial control system.

The invention discloses a space-time density peak value clustering method and system based on shared neighbor weighting, and the method comprises the steps: generating a weight coefficient through quantifying the proximity of a space-time neighbor in time and space dimensions; fusing a weight coefficient, a sample density degree and a shared neighbor similarity, and innovatively calculating a shared neighbor weighted local density which can better reflect spatio-temporal data characteristics; a decision value is generated in combination with the relative distance to accurately select a class cluster center; class cluster division is carried out by adopting a dynamic priority ranking strategy based on a maximum heap priority queue, so that the boundary point distribution precision and efficiency are effectively optimized; and finally, abnormal points are identified through a self-adaptive dynamic threshold value based on statistical distribution characteristics. According to the method, the problems that traditional density peak value clustering is poor in spatio-temporal data adaptability and high in noise sensitivity are solved, and the clustering accuracy, robustness and efficiency in applications such as intelligent traffic flow analysis and regional hot spot monitoring are remarkably improved.

The invention discloses an Otsu feature enhancement-based macroalgae culture area identification method and system. The method comprises the following steps: acquiring multi-spectral remote sensing image data of a target area and preprocessing the multi-spectral remote sensing image data; generating a spatial constraint binary mask for the preprocessed image by adopting an Otsu algorithm; constructing a multi-dimensional feature space containing a spectral band, a normalized index and an Otsu segmentation result; and inputting the multi-dimensional features into a classifier for classification. According to the method, the Otsu binarization result is segmented and reconstructed into the spatial distribution features from the terminal, the classification precision is remarkably improved in multiple test scenes, the area error of seaweed monitoring is effectively reduced, the defects of feature solidification and noise sensitivity in a traditional method are overcome, and the method is suitable for near-real-time monitoring requirements in multiple scale scenes.

The invention relates to the technical field of infusion monitoring management based on central monitoring, and discloses an infusion monitoring management method based on central monitoring. Collecting the residual volume at regular time through a liquid level sensor, calculating the instantaneous flow velocity by combining the volume difference and time difference of adjacent samples, setting the order of a fractional derivative, and recursively solving a generalized binomial coefficient to obtain a derivative value; constructing a sliding window according to a preset time window, extracting a median and a median absolute deviation, and adaptively generating upper and lower limit thresholds; and comparing the derivative and the threshold value mark abnormity in real time, and regularly summarizing to generate a monitoring report. According to the integrated process, the problems of noise sensitivity, lag and missing detection of traditional monitoring are solved, fractional order derivatives strengthen tiny drift perception, robust statistics restrains interference, a self-adaptive threshold gives consideration to sensitivity and robustness, automatic high-frequency sampling and periodic reporting improve monitoring precision and response speed, manual burden is relieved, and management is optimized.

The invention belongs to the field of high-precision time calibration and navigation positioning, and discloses a rubidium atomic clock calibration method based on a Rowland system and a related device. Spectral analysis is performed based on a Welch method and an adaptive peak detection method so as to accurately identify frequency, amplitude and phase parameters of periodic components, then a mixed time difference model including a secondary trend term, a multi-period term and a noise term is constructed, and the model is solved by adopting a least square method so as to obtain key performance parameters of the rubidium atomic clock. And finally completing time calibration of the Rowland system taming rubidium clock. By adopting the method, the parameter estimation precision of the rubidium clock is effectively improved, the problems of mode aliasing, noise sensitivity, non-unique result, low calculation efficiency and the like are solved, and reliable and real-time calibration of the rubidium atomic clock by the Roland system is realized.

The invention discloses a white spirit flavor recognition method based on machine learning, and belongs to the technical field of food detection and artificial intelligence. Aiming at the problems of high subjectivity of manual evaluation, low efficiency of mass spectrometry, noise sensitivity of a machine learning model and the like in the prior art, the method provides a solution integrating solvent background deduction and feature weight screening. The method specifically comprises the following steps: diluting a white spirit sample with methanol according to a volume ratio of 1: 10, collecting mass spectrum data through GC-MS, and dynamically deducting a methanol background peak; a BP neural network (GABP) optimized by a genetic algorithm is utilized to automatically analyze a weight coefficient of each molecular peak to flavor classification, and key features are screened; a classification model is constructed based on XGBoost, and parameters are optimized through cross validation, so that automatic judgment of the flavor type, authenticity and quality of the white spirit is realized. According to the method, through data dimension reduction and model collaborative optimization, the overfitting problem caused by high noise and high redundancy of mass spectrum data is solved, the classification accuracy is remarkably improved, and the method can be extensively applied to white spirit brand identification, process optimization and market quality supervision.

The invention discloses an AD gait anomaly detection method based on wavelet transform and ARMA modeling. The AD gait anomaly detection method comprises the following steps that gait data and HRV data of a patient are collected and preprocessed; performing feature extraction on the preprocessed gait data by using a wavelet transform function; an ARMA model is constructed by combining the self-correlation function and the partial self-correlation function with the HRV data of the patient, a fusion model is constructed based on the ARMA model and an LSTM neural network, the AD gait anomaly degree of the patient is detected through the fusion model, and different levels of risk signals are sent according to the detection result. The method has the beneficial effects that the problems of noise sensitivity, short prediction period, insufficient specificity and the like in AD gait detection are solved through a three-order technical chain of wavelet transform-ARMA modeling-nonlinear judgment. More accurate AD gait anomaly detection is realized, so that a patient can receive early warning and carry out intervention in an AD early stage in real time.

The invention belongs to the technical field of natural language processing and text mining, and discloses an online forum-oriented low-resource topic key topic extraction method, which comprises the following steps of: performing semantic-preserving data enhancement on an original text through a large language model to generate an enhanced document set; utilizing a pre-training language model to extract context-aware semantic representation of the document; constructing a learnable topic embedding matrix, and calculating and generating topic distribution; designing a semantic perception contrast learning framework, and optimizing theme diversity by adopting a dynamic negative sample screening strategy; and meanwhile, priori alignment loss is used for ensuring theme consistency. According to the invention, an LLM enhanced data expansion mechanism and a lightweight theme coding architecture are creatively fused, and through dual optimization of contrast learning regularization and prior distribution matching, three technical problems of data sparsity, model over-fitting and noise sensitivity in a low-resource scene are effectively solved; and an efficient and reliable theme modeling solution is provided for social media public opinion analysis.

The invention discloses a thermal hydraulic flow field prediction method based on an evidence physical information neural network, and the method comprises the specific steps: collecting different physical quantity data sets in a thermal hydraulic system, and carrying out the preprocessing of the data; constructing data loss; constructing physical loss; constructing an evidence uncertainty modeling mechanism, and dynamically allocating evidence weights according to the reliability of different data and physical constraints; constructing a total loss function by integrating the data error, the physical residual error and the evidence reliability; and outputting the trained neural network model and prediction. According to the invention, by introducing an evidence learning mechanism, the uncertainty of the model is dynamically estimated in the network training process, and the adaptability of the model to data with inconsistent noise sensitivity degrees is significantly improved; the uncertainty of the evidence is mapped into a physical quantity weighting coefficient, a uniform normalized weighting loss function is constructed, and the subjectivity and robustness problems caused by manual setting of hyper-parameters for each physical quantity in a traditional physical information neural network are effectively avoided.

The invention discloses a high-speed rail line gale prediction system and method, and relates to the technical field of high-speed rail gale prediction. The system for predicting the gale along the high-speed rail comprises a multi-scale grouping representation module, a comprehensive prediction module and a data enhancement module, the multi-scale grouping representation module is used for generating a wind speed preliminary prediction value; the comprehensive prediction module is used for generating a final wind speed prediction value; the data enhancement module expands the diversity of the original wind speed data set. Based on the high-speed rail line gale prediction system, the high-speed rail line gale prediction method comprises the following steps: grouping multi-scale data; carrying out multi-dimensional attention feature fusion; calculating a preliminary predicted value; calculating a final predicted value; carrying out combined optimization training; expanding an original wind speed sample; and performing consensus enhancement training. The method has the advantages that through collaborative design of multi-scale feature fusion, dynamic adaptive weighting and unsupervised enhanced training, the three core problems of sudden wind speed report missing, noise sensitivity and marking dependence in high-speed rail gale prediction are solved, and the operation efficiency and safety of high-speed rail lines are improved.

The invention relates to the technical field of artificial intelligence, in particular to an online test adaptation method and device for time sequence prediction. Comprising the following steps: establishing and maintaining a historical sample memory bank, storing historical time sequence data, and updating the memory bank through a first-in first-out strategy; screening a historical sample set in a historical sample memory bank, wherein the similarity between the historical sample set and the test sample in the potential space meets a preset condition; performing frequency domain-based mixed data enhancement on the test sample and the historical sample set to generate an enhanced sample set; inputting the enhanced sample set into a time sequence prediction model for batch training, dynamically adjusting model parameters to adapt to distribution offset, and outputting a final prediction result generated through fusion; and evaluating the performance of the model through the loss function. According to the method, the adaptability of the model to new distribution can be enhanced, the robustness of the model is improved, the noise sensitivity is suppressed, data enhancement is performed on the time sequence, and damage to a time domain of the time sequence is avoided.

The invention discloses a multi-modal learning-oriented adaptive differential privacy budget adjustment method, which dynamically allocates a differential privacy budget in a training process by evaluating the feature distribution difference, task contribution degree and noise sensitivity of multi-modal data, and specifically comprises the following steps of: extracting a multi-modal feature vector and calculating modal correlation; fusing features and predicting downstream task results; determining a contribution ratio and a sensitivity difference ratio based on the prediction accuracy and the noise sensitivity; the privacy budget of each round is finely adjusted in combination with the correlation, so that the high-contribution mode distributes the large budget to guarantee the performance, and the low-contribution mode distributes the small budget to strengthen privacy protection; gaussian noise is injected according to a budget in gradient back propagation.

An electric power system inertia identification method based on ARMAX and adaptive recursive optimization comprises the steps that active power and frequency data signals at a bus are collected in real time, the data signals are preprocessed, and a standard data set is formed; establishing an active-frequency dynamic response model of the new energy power system, and deducing frequency response transfer functions of the system in large and small disturbance scenes; an ARMAX model is constructed, and the coupling relation between parameters of the ARMAX model and the inertia is established; and solving parameters to be identified in the ARMAX model by adopting an adaptive recursive least square algorithm. Designing a fuzzy controller to carry out online correction on the adaptive forgetting factor, and calculating to obtain an optimal parameter vector; and substituting the optimal parameter vector into the ARMAX model, and calculating the inertia time constant of each unit. The method solves the problems of data saturation and noise sensitivity of a traditional method; and meanwhile, the inertia identification precision is improved through a time-varying gain matrix and a covariance iteration mechanism, and full-scene coverage from noise-like small disturbance to transient large disturbance is realized.

The invention discloses a differential privacy access control method and system based on dynamic self-adaption. The method comprises the following steps: firstly, collecting user role information, query complexity and access request data, and dynamically calculating a privacy budget according to different user roles and query complexity; then, statistical characteristics of all continuous fields in the data set are extracted, and an optimal noise sensitivity parameter s is obtained through training by adopting a multi-layer perceptron (MLP) deep learning model in combination with a gradient descent algorithm; adding noise sampled from Laplacian distribution into the original query result according to the proportion of the privacy budget to the sensitivity parameter through a noise adding formula; finally, the system dynamically adjusts the user access authority according to the accuracy of the data after noise processing, and dynamic balance between data privacy protection and data practicability is achieved. According to the method, the data accuracy change caused by the privacy budget is used as a data practicability index, and the change of the accuracy inferred by the model is used for reflecting the privacy protection effect, so that the influence of the privacy budget value on the data utility and privacy protection can be accurately evaluated, the optimal privacy budget is selected for each user in different access scenes, and the user experience is improved. The user privacy security is guaranteed to the maximum extent, the influence of noise on data accuracy is reduced to the minimum extent, and the optimal balance between privacy protection and data utilization is achieved.

The application relates to the technical field of system-in-package, and discloses a functional chip SIP (System in Package) system-in-package method and system. The method constructs a reduced-order discrete thermal state predictor through multi-order exponential attenuation fitting, generates a DVFS gear-noise hazard degree spectrum mapping table based on a synchronous switching noise spectrum and an ADC noise sensitivity curve, generates a multi-DVFS working condition robust grounding network topology by optimizing a narrow bridge connection structure parameter by using a sequential quadratic programming, quantizes equivalent noise interference amounts of each DVFS gear on an ADC chip, and jointly optimizes DVFS gear selection and power consumption upper limit distribution under a rolling time domain mixed integer quadratic programming framework, simultaneously performs online model correction through exponential weighted moving average and Kalman filtering, and realizes cooperative satisfaction of thermal constraints and noise constraints.

A kind of atomic gyroscope pumping rate stable composite control method based on phase reshaping extended state observer and power approaching sliding mode control.Aiming at the problems that standard linear extended state observer has steady-state estimation error under the action of ramp disturbance, disturbance estimation phase lag and it is difficult to balance between observation bandwidth improvement and noise sensitivity, a composite corrector composed of lead network and lag network is cascaded at the output end of disturbance estimation of extended state observer, the disturbance estimation channel is shaped in frequency domain, phase lead is provided in mid-frequency band to reduce compensation delay, amplitude lag characteristic is used to suppress high-frequency noise, and parameter constraint design is used to realize zero-error estimation of ramp disturbance;At the same time, a power approaching sliding mode controller is constructed to improve the adaptability to the nonlinear, internal and external coupling and parameter uncertainty of atomic gyroscope system, and the phase reshaping extended state observer is combined to effectively estimate and feed forward compensate the lumped disturbance, so as to improve the stability and accuracy of pumping rate control.The present application can balance dynamic anti-disturbance performance, steady-state control accuracy, convergence speed and noise robustness without significantly improving the observation bandwidth, and is suitable for atomic gyroscope precision control system affected by ultra-low frequency slowly varying disturbance.

The invention discloses a knowledge tracking method and system based on cognitive decoupling, and belongs to the technical field of knowledge tracking, and the method comprises the steps: carrying out the coding of historical learning interaction data of students, and generating a question feature vector and an answer feature vector; respectively decomposing the question feature vector and the answer feature vector into a stable cognitive mode component and a random factor component; performing time sequence modeling on the stable cognitive mode component based on an attenuation attention mechanism, and simulating short-term disturbance of the random factor component to the knowledge state based on the attenuation attention mechanism to obtain dynamic knowledge state representation; and according to the dynamic knowledge state representation and the current question feature vector, predicting the correct answering probability of the student to the next question. The method solves the problems of noise sensitivity, insufficient dynamic modeling, single feature representation and the like in the prior art, and finally realizes higher prediction precision, stronger robustness and finer-grained knowledge state tracking.

The application discloses an improved extended state observer method based on frequency domain disturbance compression, which comprises the following steps: an extended state observer designed for an optoelectronic tracking system is reconstructed from a frequency domain perspective, a frequency domain disturbance compression algorithm is implemented, and a disturbance compression extended state observer is obtained; the disturbance compression extended state observer is converted into a frequency domain form in combination with a linear feedback controller; the stability of the disturbance compression extended state observer and the stability of a closed loop system are analyzed to obtain stability constraint conditions; a parameter setting method of the disturbance compression extended state observer is set; and on the basis of meeting the stability constraint conditions, the influence of the disturbance compression extended state observer on disturbance suppression and noise sensitivity is analyzed. The application effectively reduces the upper limit of the disturbance and its derivative by changing the disturbance characteristics perceived by the observer, significantly improves the estimation accuracy of the state and the disturbance, and improves the anti-interference ability and noise insensitivity of the system.

The present application discloses a partial discharge detection method, device, electronic device, and storage medium. The method and device are applied to an electronic device, specifically for collecting acoustic signals generated by partial discharge of a high-voltage electrical device to be detected to obtain acoustic data; based on the acoustic data, high-frequency acoustic signals are extracted through high-pass filtering, and the power spectral density of the energy envelope of the high-frequency acoustic signals is calculated; based on the energy distribution of the power spectral density of the energy envelope at a specific frequency, the discharge type of the high-voltage electrical device is identified. This solution is robust to external noise interference and effectively overcomes the problem of noise sensitivity existing in traditional partial discharge detection methods based on acoustic emission technology (such as PRPD pattern analysis), thereby significantly improving the detection ability for weak discharge signals.

The invention provides a medical invoice edge detection model construction method and device, equipment and a medium, and the method comprises the steps: obtaining a medical invoice image set, and marking the invoice semantic anchor point of each medical invoice image in the medical invoice image set, so as to construct a sample set; the mask auto-encoder is improved, and an optimized mask auto-encoder is obtained; performing iterative training on the optimized mask auto-encoder by using the sample set to obtain a trained mask auto-encoder, and extracting a multi-layer residual network from the trained mask auto-encoder; constructing an initial edge detection model based on a multi-layer residual network and a double-branch attention fusion module; and training the initial edge detection model by using the sample set to construct a medical invoice edge detection model. By adopting the medical invoice edge detection model construction method and device, the equipment and the medium, the noise sensitivity is reduced, the edge feature extraction capability is improved, and the image detection precision is improved.

The invention discloses a silence cabin intelligent control method and system based on an AI digital matrix, relates to the field of silence cabin intelligent control, and constructs a high-dimensional silence cabin multi-modal feature tensor, namely the AI digital matrix, by obtaining multi-source heterogeneous data such as environment, biological features and equipment states and using time synchronization, matrix construction and normalization cleaning technologies. On the basis, performing user activity scene semantic analysis on the feature tensor to generate an activity weight vector reflecting the current demand of the user; and then prediction modeling of the acoustic-thermal coupling effect is carried out based on the weight vector, optimization is carried out in a prediction result set by using a multi-target Pareto optimal algorithm, and optimal control parameters giving consideration to noise sensitivity, thermal comfort and energy consumption are calculated. And finally, a PWM signal is generated through closed-loop feedback to drive a fan and light, so that the breathing effect of the fan is eliminated on the physical level, and active precise regulation and control of the environment of the mute cabin are realized.

The application provides a spectral submanifold-based fast and accurate control method for a cable-driven tensegrity robot, and belongs to the field of control of the cable-driven tensegrity robot. First, end position data is taken as sampling data, data in the direction of the z coordinate axis is removed from the truncated data to obtain training data after truncation. Second, according to the Takens delay embedding theorem, the training data is projected onto a spectral subspace, a spectral submanifold is parameterized, a reduced-order dynamics is obtained, and an optimal solution is found. Finally, the position data and control input of the sampling end center point are open-loop controlled, the data of the end center point is projected onto the spectral subspace, is converted into an optimal problem to learn a control matrix, and then an optimal control problem is constructed. The application has low dependence on high-dimensional data, weak noise sensitivity, strong robustness of the reduced-order model, can efficiently realize fast and accurate control of the end pose of the cable-driven tensegrity robot, and has strong adaptability and expansibility.