179 results about "Local linear" patented technology

The invention provides a network security monitoring method and system based on distributed nodes, and the method comprises the steps: obtaining an inter-node interaction record of each node in a distributed network in a preset communication period and the identification information in a protocol interaction process, and obtaining a node communication data set; the method comprises the following steps: constructing a multi-dimensional phase space reconstruction matrix containing communication time sequence association features and protocol identifier association features, carrying out nonlinear dynamic feature analysis on the matrix, extracting a chaotic feature value set of node communication behaviors, constructing a topological association structure of node communication features through a local linear relationship, mapping the set to a topological space of a preset dimension, and constructing a topological structure of the node communication behaviors. And generating an attack mode topology expression vector, and finally analyzing an abnormal state propagation process in the distributed network through an inter-node energy propagation rule based on the attack mode topology expression vector to obtain a network security detection result. According to the invention, the accuracy and dynamic analysis capability of distributed network security monitoring can be effectively improved.

The invention relates to an edge data processing method of an information security monitoring and early warning AI platform and a related device. The method comprises the following steps: carrying out standardization processing on original security alarm data collected by a plurality of edge computing devices to obtain standardized feature data; constructing a target function comprising a processing efficiency item and a resource consumption item based on the standardized feature data, and decoupling the target function into a plurality of sub-optimization problems of edge computing nodes by using a Lagrange multiplier to obtain an initial task allocation scheme; performing local linearization processing and convex optimization decomposition on the initial task allocation scheme to obtain an optimal task decomposition scheme; constructing a state transition probability matrix according to the optimal task decomposition scheme, and generating a dynamic task scheduling strategy; and distributing the dynamic task scheduling strategy to each edge computing node, and performing time sequence analysis and spatial clustering on multi-source data to obtain a security situation assessment result. According to the invention, the utilization efficiency of edge computing resources is improved.

The invention belongs to the technical field of image processing, and particularly relates to an oil and gas engineering supporting facility intelligent detection method based on machine vision, and the method comprises the steps: constructing a structure tensor matrix based on the gradient of pixel points, calculating a local linear structure response value according to two feature values of the structure tensor matrix, and calculating a local linear structure response value; according to the direction alignment degree of the feature vectors of the pixel points on the local path, the crack continuity of the pixel points is calculated, the morphological significance values of the pixel points are comprehensively obtained and used for adjusting basic contrast parameters, anisotropic diffusion processing is carried out on the to-be-detected image of the oil and gas facility based on the obtained self-adaptive contrast parameters, and the to-be-detected image of the oil and gas facility is obtained. An enhanced image is obtained through multiple times of iterative updating, and a defect detection result graph is obtained through edge detection. According to the method, the problem that false defects are easily misjudged as cracks in a traditional method is solved, and the detection accuracy and reliability are remarkably improved.

The invention relates to the technical field of power plant intelligent adjustment, and discloses a power plant load intelligent adjustment method and system. The method comprises the following steps: distributing and deploying sensors according to field equipment of a power plant, and constructing a distributed sensor network based on the sensors; establishing a single time scale control framework according to the distributed sensor network; a heterogeneous delay compensation mechanism is constructed based on a distributed sensor network and a single time scale control framework; constructing a local linear matrix inequality constraint condition of each node, obtaining an optimal solution of the local linear matrix inequality constraint condition according to a heterogeneous delay compensation mechanism, and then generating an optimal control strategy of each node; the method comprises the following steps: constructing a global Lyapunov function based on a distributed sensor network and a single time scale control framework; and the change rate of the global Lyapunov function is monitored in real time, and corresponding equipment is intelligently and dynamically adjusted according to the optimal control strategy. The method achieves the intelligent regulation and control of the power plant load, is good in stability, is high in adaptability, and is short in regulation and control period.

The invention discloses a data aggregation method based on multi-modal features, which comprises the following steps: collecting multi-modal data, preprocessing the multi-modal data, extracting features according to modals, dividing the features into high-dimensional data, medium-dimensional data and low-dimensional data, and positioning neighbor points by using a ball tree algorithm for the high-dimensional data; the neighborhood range of the medium-dimensional data is dynamically adjusted based on the distribution density; a neighborhood of low-dimensional data is calculated through Euclidean distance, then the low-dimensional data is mapped to a low-dimensional space by means of local linear embedding, then the low-dimensional data is traversed, discrete data value frequency and continuous data probability density are counted, marginal probability is calculated in combination with information entropy, data aggregation weight is determined accordingly, and finally features after dimension reduction are spliced according to a high-level sequence, a middle-level sequence and a low-level sequence. And probability normalization is carried out in each hierarchical block to generate aggregated comprehensive features. According to the method, through multi-dimensional differentiation processing and weight calculation based on data distribution, aggregation of multi-modal features is realized, and feature complementarity and accuracy are improved.

The invention relates to the technical field of 3D modeling, and discloses a 3D modeling-based teenager body posture intelligent evaluation method, which comprises the following steps of: 1, performing spatial modeling on teenager body postures by using a topology method, and representing the teenager body postures as a high-dimensional manifold space, the topological relation between postures in the posture space is captured by conducting homology group analysis on the high-dimensional manifold space; and 2, after the topological structure of the body posture of the teenager is obtained, carrying out dimension reduction on high-dimensional posture data by applying a manifold learning technology, and optimizing the expression of the data by using a local linear embedding method. Through combination of a topology method and homology group analysis, modeling of teenager body postures in a high-dimensional manifold space is realized, topological relations among postures in a posture space can be effectively captured, a 3D posture model is obtained, and the problem that complex posture changes cannot be accurately described in the prior art is solved.

The invention discloses a new engineering course teaching evaluation method based on a knowledge-ability-quality triple atlas, and belongs to the technical field of intelligent education and intelligent control. Collecting data of three dimensions of knowledge, ability and quality of students to construct a unified state vector, and describing dynamic evolution of a learning process by combining a state updating and prediction model of a residual network; a multi-cell filtering method is adopted to carry out state set estimation, linear propagation and residual local linearization are combined in the prediction step, and a Lipschitz upper bound is utilized to carry out external connection on a nonlinear residual to ensure the safety and credibility of state interval estimation; in the updating step, set tightening is achieved through prediction strip intersection and generator contraction, coverage rate calibration based on quantiles is introduced, and the confidence level of set estimation is ensured. According to the method, the learning state of the student can be dynamically, stably and interpretably estimated, accurate and efficient course adjustment optimization is realized, and the method has a good application prospect and popularization value.

The invention discloses a source-load interval prediction method based on the combination of SWLSTM and integrated learning, and the method comprises the following steps: S1, obtaining an original data set, carrying out the dimension reduction of the original data set based on local linear embedding, and carrying out the clustering of the data set after the dimension reduction based on a k-means algorithm, and obtaining a feature day data set; s2, training each base learner based on the feature day data set, respectively calculating Q statistical values among the results of each base learner, and screening out a base learner pair with the minimum Q statistical value as a base learner model; s3, taking the base learner model as a first-layer prediction model of an integrated learning framework, taking the SWLSTM neural network as a second-layer prediction model, and introducing a confidence interval to obtain prediction uncertainty information; and S4, based on the point prediction result and the uncertainty prediction result obtained by prediction in the step S3, evaluating the prediction result by adopting a point prediction evaluation index and an interval prediction index respectively.

The invention discloses a composite material structure impact positioning method based on wavelet packet Tsallis entropy and LLE, and the method comprises the following steps: employing an EFPI optical fiber sensor network to monitor impact response signals at different positions on a structure, extracting wavelet packet Tsallis entropy spectrums of the impact response signals, carrying out dimension reduction on the wavelet packet Tsallis entropy spectrum by adopting a local linear embedding method; according to the relation between the wavelet packet Tsallis entropy spectrum of the impact point response signal after dimension reduction and the impact point position, impact position identification is achieved through a GA-CNN-BiLSTM-Attention model; according to the method, the wavelet packet Tsallis entropy spectrum of the impact response signal monitored by the EFPI sensor on the composite material structure is analyzed, the feature vector is extracted from the wavelet packet Tsallis entropy spectrum by using the LLE technology, and the impact load position of the composite material structure is accurately identified by using the GA-CNN-BiLSTM-Attention neural network model, so that the method has the advantages of no need of a high-speed data acquisition module, small signal feature data volume, high accuracy and the like. The application condition is wide, and the identification model generalization ability is good.

The invention relates to the technical field of bridge safety monitoring, and particularly discloses a cable-stayed bridge cable force unbalance vortex vibration early warning system and method based on an intelligent calculation model, and the system comprises a multi-source data collection module, a phase-space reconstruction module, a vortex vibration mode decoupling module, a dynamic threshold setting module, a nonlinear prediction module and an early warning decision module. The vibration signals are mapped to a multi-dimensional phase space according to the phase space reconstruction theory, and multi-order frequency band vortex vibration modal decoupling is achieved in combination with density clustering analysis; establishing a multi-dimensional threshold hypersurface by adopting a support vector machine, and dynamically adjusting a judgment threshold according to the environmental parameters and the structural state; short-term high-precision prediction and medium-and-long-term trend prediction are realized by using a local linearization prediction model; a wind-vortex vibration mapping relation is established through the generative adversarial network; the self-adaptive filtering technology is adopted to eliminate traffic load interference, and the problems that in the prior art, cable force unbalance vortex vibration cannot be accurately recognized, early warning is not timely, and the false alarm rate is high are effectively solved.

The invention relates to the technical field of intelligent control of an Isa furnace smelting process, in particular to a soft measurement model construction method for Isa copper smelting process parameters, which comprises the following steps: carrying out data sampling on an Isa furnace; constructing a local linear embedding algorithm of kernel clustering, and processing sampling data; constructing an adaptive model based on generalized maximum entropy regression; a hidden Markov model is adopted to predict working conditions in the sampled data, and optimal parameters of the adaptive model based on generalized maximum entropy regression are obtained; and inputting the optimal value into the model, inputting a processed result into the model after the optimal value is obtained, and outputting a final prediction result. The prediction result is basically consistent with the actual production result, the prediction correctness and stability are good, and the method has reference value for optimization control of the actual production process.

The invention discloses a nonlinear model predictive control method for the strip steel temperature of a continuous annealing furnace, and relates to the technical field of metallurgical industry automation control, and the method comprises the following steps: collecting historical data in the production process of the continuous annealing furnace; a nonlinear autoregression exogenous input model based on Sigmoid-ARX is established; the model parameters are optimized through a Levenberg-Marquardt algorithm, and the model parameters are optimized through the Levenberg-Marquardt algorithm; based on the optimized Sigmoid-ARX model, designing a nonlinear model prediction controller, performing local linearization near a given working point, and converting a nonlinear optimization problem into a quadratic programming problem; by constructing the Sigmoid-ARX nonlinear model and the predictive control capability and system-level constraint processing characteristics of the model predictive control algorithm, the strip steel temperature predictive precision is improved, stable control over strip steel of different specifications in the transition stage is achieved by optimizing the control strategy, the standard deviation of the strip steel temperature and speed is reduced, and the stability of the strip steel temperature and speed is improved. The average speed of a production line is improved, and the yield is increased.

The invention relates to the technical field of image data processing, in particular to an induction furnace coil temperature field visual monitoring method based on thermal imaging, and the method comprises the steps: obtaining the three-dimensional temperature field data of an induction furnace coil, and extracting high-temperature voxels; an adaptive weight is constructed for each high-temperature voxel, and the weight is composed of two parts: 1, the local linearity of the voxel is determined by calculating the projection density of the voxel in multiple directions; secondly, obtaining a colinearity score by calculating the consistency of the candidate voxel and other candidate voxels in the neighborhood of the candidate voxel in the local main direction; and performing weighted Hough transform on the candidate voxels to vote by taking the product of the two as an adaptive weight. Through the intelligent weighting mechanism, real linear fault signals can be effectively amplified, noise interference is suppressed, and the detection accuracy and robustness of early discontinuous linear high-temperature faults are improved.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The invention relates to the field of fan spindle state monitoring, and aims at synchronously acquiring sound and vibration signals through multiple channels, realizing nanosecond time alignment by adopting a precision time protocol, and performing denoising and normalization preprocessing on the signals to improve data integration and signal fidelity. Furthermore, short-time Fourier transform and continuous wavelet transform are combined to extract multi-scale time-frequency features, and a high-dimensional combined feature vector is generated in combination with cross-correlation analysis. And mapping the feature vectors to a low-dimensional manifold space through a local linear embedding algorithm, and constructing a dynamic mode reference template. Indexes such as curvature, track length and direction entropy are monitored in real time, whether the spindle has an abnormal evolution trend or not is judged through a self-adaptive curvature threshold, and abnormity judgment is achieved in combination with track backtracking verification. According to the scheme, the abnormal starting boundary of the spindle state can be caught in a refined mode, and the early warning and operation and maintenance response capacity of fan operation is effectively improved.

The invention belongs to the technical field of fault testing, and provides a speed reducer sensor layout optimization method, system and device and a storage medium, and the method comprises the steps: carrying out the local linear processing of fault features, and obtaining a reduced fault feature matrix; measuring the sensitivity of the measuring points based on the fault feature matrix; and judging whether the fault sensitivity degree of the measurement point meets a target or not based on the measurement point sensitivity measurement, if so, ending, otherwise, resetting the measurement point until the fault sensitivity degree of the measurement point meets the target. A plurality of measuring points are arranged on a speed reducer fault simulation experiment platform, then fault features of the measuring points are extracted, local linear embedding processing is carried out on the fault features to obtain a reduced three-dimensional vector, the convolutional neural network is used for carrying out index evaluation on the fault sensitivity degree of the measuring points, and the fault sensitivity degree of the speed reducer is evaluated. Therefore, the sensor layout is optimized based on the evaluation result, and the accuracy and efficiency of fault monitoring are improved.

The invention relates to the technical field of computer-aided engineering, and discloses an intelligent water conservancy design simulation system based on digital twinning, which comprises a parameterized geometric feature extraction module, a reduced-order model database, a tangent space linear deduction module and a flow field reconstruction module. According to the method, nonlinear flow field solution is dimensionally reduced into deterministic matrix vector multiplication, traditional statistical regression and iterative solution are replaced, it is ensured that a reconstruction result evolves in the tangential direction of a physical gradient, non-physical guess of a statistical model in a sparse sample area is avoided, and the calculation accuracy is improved. And high-fidelity flow field reconstruction with real-time performance and physical evolution conservation is realized.

The invention discloses a semi-analytic solution-based energy flow calculation method and system for an electricity-gas integrated energy system, and the method comprises the steps: firstly building an ordinary differential-algebraic model of an electricity-gas network based on a numerical boundary extrapolation and semi-discrete method; based on a semi-analytical theory of differential transformation, establishing a linearized electricity-gas network model; carrying out block calculation on a matrix and self-adaptive adjustment on a time window; and finally solving the model to obtain an energy flow calculation result. According to the method, the original nonlinear partial differential equation of the gas network is converted into the linear algebraic equation, the complex nonlinear problem in traditional gas network modeling is simplified, errors caused by local linearization and time difference are avoided, the calculation scale is reduced, and the calculation robustness is improved.

The invention relates to a dynamic light beam shaping system and method based on a spatial light modulator. The system comprises a laser light source module, a beam expanding collimation light path, a light beam modulator, a focusing optical assembly, a detection and feedback module and a control and optimization module. According to the method, the uniformity of the output flat-topped beam can be corrected by virtue of a random parallel gradient descent (SPGD) algorithm and combining a Zernike polynomial as a control variable. Meanwhile, a composite performance index comprehensively considering energy distribution and shape features is constructed, the correction process of the flat-topped beam is accelerated by adopting a mode of gradually improving Zernike polynomial orders in multiple stages, the convergence trend of the performance index is used as a judgment basis of stage switching, stage independent variable dimension expansion is implemented, and the correction precision of the flat-topped beam is improved. And the convergence speed is obviously improved. Besides, adaptive selection of disturbance amplitude and learning rate parameters in the SPGD algorithm is realized by using noise estimation and a local linearity index, and the robustness of the system to noise and environmental disturbance is enhanced.

The invention belongs to the technical field of autonomous underwater vehicle numerical simulation, and discloses an AUV physical field prediction method based on manifold learning and deep learning, comprising the following steps: performing nonlinear dimension reduction on high-dimensional simulation physical field data based on an Isomap manifold learning algorithm, and extracting low-dimensional manifold features; constructing a deep neural network (DNN) model, and establishing a mapping relation between the working condition parameters and the low-dimensional manifold features; inputting new working condition parameters to the trained deep neural network model, and predicting corresponding low-dimensional manifold features; isomap inverse mapping is realized based on a local linear embedding algorithm, and predicted low-dimensional manifold features are reconstructed into high-dimensional physical field data. Through collaborative prediction of manifold learning and deep learning, the problem that a traditional method is low in calculation efficiency in high-dimensional physical field prediction is solved, prediction precision and real-time performance are remarkably improved, and an efficient tool is provided for AUV design optimization and dynamic control.

The invention provides a fault diagnosis method fusing adaptive wavelet threshold denoising and auto-encoder contribution degree weighting. The method comprises the following steps: carrying out wavelet denoising and standardization processing on training data in a training set based on an adaptive threshold function; using the trained residual error neural network ResNet to extract off-line related features; establishing an MSDAE off-line detection model, taking off-line related features corresponding to a plurality of modes as input for training, and calculating SPE and SPE control limits; acquiring an online test set, extracting online fault related features by using the trained residual neural network ResNet, calculating a Bayesian fusion index BIP according to the online fault related features, and judging whether online data have faults or not; and when a fault occurs, calculating the local linear propagation contribution degree of each process variable in the online data, establishing a contribution heat map, and performing fault diagnosis on the online data according to the contribution heat map. According to the invention, high-frequency noise components can be effectively filtered, and key variables causing process anomalies can be accurately positioned.

The invention discloses an electricity-gas integrated energy system scheduling method based on a global linearization gas network model, and the method comprises the steps: generating a gas network pipeline operation data set, and building a global linearization gas network model based on a Koopman operator theory; then, a Koopman operator is approximated, and an integrated energy system scheduling model is constructed; and finally solving the model to obtain an optimal scheduling result. According to the method, an original nonlinear partial differential equation of the gas network can be converted into a linear algebraic equation, the complex nonlinear problem in traditional gas network modeling is simplified, errors caused by local linearization and spatial difference are avoided, the linear algebraic equation can be easily integrated into a scheduling model of a comprehensive energy system, and the method is suitable for large-scale popularization and application. And the dynamic behavior of the natural gas system can be accurately captured, and the scheduling accuracy and reliability are remarkably improved. In addition, according to the method, errors possibly generated by a traditional model in a complex gas dynamics scene are effectively reduced, and system potential safety hazards caused by inaccurate models are avoided.

The invention provides a lithium battery cargo abnormal temperature rise identification method, and belongs to the technical field of lithium battery customs detection.The lithium battery cargo abnormal temperature rise identification method comprises the steps that a temperature sensor array is arranged on the surface of a lithium battery cargo stacking body to collect multi-point temperature time sequence data, and trend characteristics are extracted through wavelet packet decomposition denoising and singular spectrum analysis; establishing a state space model based on a heat conduction partial differential equation, estimating an internal temperature field state by using Kalman filtering, solving a heat conduction inverse problem by using a conjugate gradient regularization iterative algorithm to invert an internal three-dimensional temperature distribution field, and inputting an inversion result and statistical characteristics into a thermal anomaly identification model fused with manifold learning. Dimensionality reduction is carried out through a local linear embedding algorithm, a mahalanobis distance is calculated in a low-dimensional manifold space, an abnormal temperature rise risk score is output, when the score exceeds a preset threshold value, early warning is triggered, and the technical problem that the abnormal temperature rise in the lithium battery cargo stacking body is difficult to accurately recognize through surface temperature measurement is solved.

The invention provides a model-free prediction energy storage converter voltage control method based on a Kalman observer, and the method is characterized in that the method comprises the following specific steps: 1, constructing a super-local model of a dual-active bridge converter, and simplifying the system description through local linearization; 2, designing a Kalman observer to estimate and compensate lumped disturbance in real time, and combining dynamic matrix updating and gain adaptive adjustment; 3, designing a cost function to solve an optimal shift ratio, and realizing model-free prediction voltage control; according to the control method provided by the invention, the robustness of the converter under the conditions of model mismatch and working condition change is effectively improved, and meanwhile, a good dynamic response characteristic can be kept.

The invention discloses a water quality index multi-model short-term prediction method and system. The method comprises the following steps: acquiring water quality index time sequence data and preprocessing; respectively inputting the preprocessed data into a local linear regression model (LWLR), a dynamic linear model (DLM) and a simple exponential smoothing model (ESE) for prediction; and carrying out weighted fusion on prediction results to obtain a short-term prediction value. According to the method, the historical data of the water quality indexes are used as input, the capability of predicting future changes in a short term is achieved, and the prediction precision and adaptability can be improved.

The invention belongs to the technical field of model simulation, and particularly relates to an efficient dynamics simulation analysis method based on a Fourier neural operator. The present invention proposes FNO-Speed, and a series of comprehensive solutions for inefficient operations that the FNO solver does not fully utilize hardware. According to the method, two unique optimization methods are adopted, and comprise a multi-level parallel implicit image-to-column general matrix multiplication optimization strategy and a user-defined size high-frequency signal filtering algorithm. According to the method, efficient general matrix multiplication is achieved through an implicit image-to-column and data division strategy to replace pointwise convolution, and fragmentary calculation of frequency domain local linear transformation is eliminated through the latter. The FNO-Speed makes full use of the memory bandwidth, improves the calculation efficiency, and aims to solve the problems of low utilization rate of calculation resources and delay influence caused by large-scale data access calculation.

The invention discloses a multi-angle laser radar vertical two-dimensional wind field inversion method. The method comprises the following steps: firstly, setting a measurement center and sampling parameters of a measurement area; acquiring multi-angle radial wind speed data; introducing a multi-point local sampling strategy to each inversion center point, and establishing a local multi-constraint inversion equation set by selecting a plurality of observation points close to the spatial position of the center point and combining the observation angle and radial wind speed information; according to the method, the limitation that a traditional wind field inversion method depends on a large-range overall consistency hypothesis is broken through, a local linear modeling method is adopted, small-scale changes, local non-uniformity and turbulence disturbance existing in an actual wind field can be better adapted, and the adaptability and modeling precision of an inversion model to a complex wind field structure are improved; the anti-noise capability, the error control and the inversion convergence are remarkably improved; the invention further provides a system, equipment and a storage medium for implementing the method.

The invention provides an efficient data acquisition method and system for an HPLC (High Performance Liquid Chromatography) dual-mode communication module based on edge calculation, and relates to the technical field of HPLC dual-mode communication data acquisition. The method comprises the steps of collecting multi-dimensional resource data of an HPLC dual-mode communication module in real time, performing feature extraction by adopting a local linear embedding algorithm, constructing a resource state evaluation model based on an extreme learning machine, evaluating a resource load state of the communication module in real time, and establishing a multi-target optimization model. Solving by adopting a non-dominated sorting genetic algorithm III to obtain an acquisition strategy solution set, selecting an optimal acquisition strategy, converting the optimal acquisition strategy into a control instruction, and adjusting data acquisition parameters in combination with predictive control and a feedback correction mechanism. According to the method, the problems of resource waste, single index and the like of an existing scheme are effectively solved by performing adaptive denoising, feature extraction and resource state accurate evaluation on the multi-dimensional data and combining multi-objective optimization of data space-time association mining and acquisition efficiency, energy consumption and reliability.

This application discloses an artificial intelligence-based method, apparatus, device, and medium for 3D cell structure reconstruction, relating to the field of cell reconstruction. The method includes determining cross-modal fusion features using a multi-scale feature encoder based on point cloud data in the same coordinate system and pre-defined biological prior features, including local feature extraction, global feature extraction based on multi-head relative self-attention, and cross-modal feature fusion; performing dimensionality reduction using an improved local linear embedding manifold learning algorithm based on the cross-modal fusion features; determining topology-enhanced high-dimensional features using a graph convolutional network based on spatial proximity, feature similarity, and biological relevance, using point cloud data in the same coordinate system, pre-defined biological prior features, cross-modal fusion features, and low-dimensional features preserving topological relationships; and performing 3D reconstruction using a decoder based on the topology-enhanced high-dimensional features. This application can improve the accuracy and mechanistic interpretability of 3D cell models.