57 results about "Tensor completion" patented technology

The invention provides a multi-source heterogeneous data dynamic updating and agent knowledge base construction method, and relates to the technical field of data processing, and the method comprises the steps: organizing heterogeneous data through a three-dimensional feature matrix, constructing feature mapping through singular value decomposition and cross decomposition, and executing recursive tensor completion to generate a fusion feature space; extracting multi-scale features and determining a stable knowledge entity based on comprehensive measurement; constructing a network structure and dividing knowledge clusters; and performing differentiation fusion of the knowledge clusters based on the life cycle parameters. According to the method, efficient integration of heterogeneous data, accurate extraction of knowledge entities and dynamic optimization of knowledge structures are realized, and the intelligent level of knowledge management is improved.

The invention provides a rock and soil construction quality monitoring and diagnosing method, which comprises the following steps of: modeling historical engineering events and expert rules, constructing an event causal atlas prototype containing weights, and realizing event sequence feature extraction and real-time causal atlas dynamic updating in combination with on-site multi-modal sensing data and construction logs; neural symbol reasoning and tensor completion technologies are adopted to predict a novel causal relationship, and a causal atlas structure is perfected through space, time and logic consistency verification; a multi-layer risk early warning mechanism is set, a causal map local risk assessment and event chain propagation are combined, spatial positioning early warning signals are generated in a grading manner, closed-loop backtracking optimization is supported, causal reasoning accuracy and early warning efficiency are improved, construction process risk identification and dynamic early warning can be realized, and the engineering safety management level is improved.

The invention relates to a water conservancy monitoring data abnormal state identification method based on multi-modal learning, and the method specifically comprises the following steps: deploying a heterogeneous sensor network at a water conservancy facility, collecting original monitoring data, combining the text modal data of a work log, and carrying out the abnormal state marking to form a training data set; constructing uniform time grid tensor alignment multi-modal data, and obtaining a complete alignment tensor by adopting a low-rank tensor completion algorithm of fusion modal mutual information constraint; constructing a machine learning model comprising a cross-modal feature collaborative enhancement module, a heterogeneous feature projection and gating fusion module and a spatio-temporal context sensing anomaly recognition module, inputting a complete alignment tensor into the model to obtain an anomaly probability value, and training the model through a loss function; new data is collected, preprocessed and input into the trained model, an abnormal probability value is compared with a set threshold value, and an abnormal type is judged. According to the method, the feature representation capability is enhanced through multi-module cooperation, and the accuracy and timeliness of water conservancy facility anomaly recognition can be improved.

The invention provides an image restoration method based on adaptive weighted tensor completion, and relates to the technical field of image processing and application, and the method comprises the steps: obtaining to-be-restored image data, and carrying out the tensor of the to-be-restored image data, and obtaining input tensor data; constructing a tensor completion model based on an adaptive weighted tensor nuclear norm; wherein a weight matrix in the tensor completion model can be adaptively updated along with input tensor data; and based on an alternating direction multiplier method or an approximate singular value decomposition method based on tensor QR decomposition, solving the tensor completion model, and outputting restored tensor data to realize image restoration. According to the scheme, the image restoration quality can be improved.

The invention relates to the technical field of data processing, in particular to a traffic data completion method and device based on a time autoregression low-rank tensor, and the method comprises the following steps: obtaining incomplete traffic data; constructing a three-dimensional tensor to represent incomplete traffic data, and obtaining an observation index set and a tensor matrix; taking a low-rank tensor completion model based on rank minimization as a framework, and introducing time autoregressive regularization and truncation weighted nuclear norms to obtain a low-rank tensor completion model; performing problem decomposition solving on the low-rank tensor completion model on the basis of an ADMM framework to obtain four variable parameters, namely, a variable parameter, a variable parameter and a variable parameter; and iteratively updating the variables according to the sequence until a preset convergence standard is reached, and outputting the complemented complete traffic data. According to the method, the time change is introduced into the completion of the three-order tensor as a new regularization item, so that the historical information can be learned during data recovery, the low-rank performance of the completed tensor can be ensured, and a complex missing scene can be better processed.

The invention relates to the technical field of electric energy dispatching, in particular to an electric energy dispatching method based on an energy storage system. The method comprises the following steps: deploying a distributed sensor array in a battery module to collect multi-source heterogeneous data, and carrying out standardization processing to generate a standardized energy storage data set; performing tensor completion on the standardized energy storage data set to generate energy storage tensor completion data; time-space dimension analysis is carried out on the energy storage tensor complemented data, cross-modal feature fusion is carried out, and an energy storage cross-modal feature space is generated; therefore, through structural processing of multi-dimensional fusion data and dynamic feedback optimization of the digital twinborn model, the problems that traditional cold chain storage environment prediction lags behind and the model adaptability is poor are solved, and the precision of temperature prediction and the intelligent level of storage management are improved.

The invention provides an edge cloud cache configuration method and device based on tensor complementation, and relates to the technical field of containerization deployment in an edge cloud network, the method comprises the steps of decomposing mirror image layer prefetching into prediction of mirror image layer cache and mirror image layer pre-scheduling, combining tensor complementation, an iTransform model and a CP decomposition algorithm, and after the tensor is complemented, obtaining an edge cloud cache configuration result. The iTransform model captures non-stationary dependence among dimensions of tensors through an inverted time sequence attention mechanism of the iTransform model while keeping high efficiency of parameters, steady inference on current time information is realized, so that an accurate mirror image layer prefetching task is supported, a CP decomposition algorithm performs tensor reconstruction on each selectable tensor to calculate a score of each selectable tensor, and the score of each selectable tensor is calculated to obtain a pre-fetching task of the mirror image layer. And finally, taking the selectable tensor with the highest score as a target tensor. Based on the method, efficient prefetching and cache optimization of the AI micro-service mirror image layer can be realized, so that challenges caused by resource limitation in an edge cloud environment are effectively relieved.

This invention relates to the field of industrial time-series data completion technology, and particularly to a method for industrial time-series data completion based on tensor weighted gamma norm. The method includes: collecting industrial time-series data through sensors and converting it into an observation tensor; processing the observation tensor for missing values ​​to obtain a tensor to be completed, and constructing a low-rank tensor completion model; defining a tensor weighted gamma norm and substituting it into the low-rank tensor completion model to obtain a low-rank tensor completion model using the tensor weighted gamma norm; solving the low-rank tensor completion model using the tensor weighted gamma norm using the alternating direction multiplier method to obtain the target solution; converting the target solution into the format of industrial time-series data to obtain the completed industrial time-series data. This invention utilizes tensor weighted gamma norm to complete industrial time-series data, and further leverages the interdependence and trends between data to mitigate the impact of the nuclear norm approximating the rank function on all singular values ​​to the same degree of contraction.

The present invention is an Internet of Things data reconstruction method based on structured low-rank tensor completion. First, the monitoring area is discretized into multiple grid points, and a sensor node is deployed inside each grid point. Assuming that the sensor node senses data every other time slot, the data received by the base station within time T forms a third-order tensor. Secondly, the data reconstruction is converted into a basic low-rank tensor completion problem, and a low-rank tensor completion model is constructed. Finally, the block Hankel matrix transformation is performed on the unfolding matrix of each mode i of the third-order tensor, and the basic low-rank tensor completion model is improved into a structured low-rank tensor completion model. The augmented Lagrangian function of the structured low-rank tensor completion model is solved to obtain the third-order tensor, and the Internet of Things data reconstruction is completed. The data collected at continuous moments are arranged in a third-order tensor to make full use of the spatial correlation of the data. The block Hankel matrix transformation is performed on the unfolding matrix of each mode i of the third-order tensor, and data reconstruction is carried out by combining structured and low-rank tensor completion, further exploring and utilizing the spatio-temporal correlation of the data, alleviating the influence of basis mismatch on the reconstruction performance in the sparse constraint-based method, and improving the data reconstruction accuracy.

The invention discloses a physiological signal extraction method based on self-adaptive ROI and ViM dual paths, which comprises the following steps: constructing a first quality perception space-time diagram of a first user and a second quality perception space-time diagram of a second user based on a self-adaptive region-of-interest selection mechanism and a low-rank tensor completion method; extracting a first time sequence prediction signal of the first quality perception space-time diagram and a second time sequence prediction signal of the second quality perception space-time diagram through a time path of the ViM dual-path network structure, and extracting a first frequency domain prediction signal of the first quality perception space-time diagram and a second frequency domain prediction signal of the second quality perception space-time diagram through a frequency path; and fusing the first time sequence prediction signal and the first frequency domain prediction signal to obtain a first physiological signal of the first user, and fusing the second time sequence prediction signal and the second frequency domain prediction signal to obtain a second physiological prediction signal of the second user. According to the method, the robustness, precision and efficiency of physiological signal extraction are improved, and different scene requirements can be considered.

The invention discloses a network measurement data completion method and device based on incomplete multi-view analysis, and the method comprises the steps: firstly decomposing a recovery problem of a fine-grained network measurement tensor into two perspectives of space consistency and time complementarity for joint modeling, constructing a deep learning model for tensor completion, and then training the model; and inputting a measurement tensor of network measurement data containing missing values into the trained model, performing forward propagation processing on the observation measurement tensor through a plurality of stages of the model, solving an optimization problem of joint modeling in an iteration mode, and outputting a recovered complete data tensor. According to the method, the tensor recovery problem is innovatively decomposed into two orthogonal views of space consistency and time complementarity for joint modeling, wherein the space view restrains a global topological structure through a graph tensor nuclear norm, and the time view captures a node heterogeneous evolution mode through CP decomposition. And a cross-node attention mechanism is further introduced to realize spatio-temporal feature alignment and fusion.

The present application relates to a kind of suitable for multiple types of mass power user data completion method, comprising the following steps: step 1, the power consumption data of multiple types of power users is collected;Step 2, the power consumption data of single different user is decomposed into the sum of trend term and periodic term;Step 3, periodic term verification is carried out;Step 4, the power consumption data of single user collected in step 1 and the periodic term, trend term time series vector after decomposition in step 2 are carried out standard delay transformation, obtain the hankel form tensor;Step 5, establish time series smoothing constraint and periodic term constraint;Step 6, establish comprehensive tensor completion objective function;Step 7, solve the comprehensive tensor completion objective function obtained in step 6 and output complete data by anti-MDT technology.The present application can be applied to one-dimensional and multidimensional data scene, and the completion effect is good.

The present invention provides a Bayesian tensor completion algorithm based on multi-measurements. The multi-measurement data is represented by multiple tensors, and it is assumed that each measurement value of each tensor element of the tensor follows a Gaussian distribution. Then, CP decomposition is performed on the tensor to obtain the corresponding factor matrices, and it is assumed that the parameters of the factor matrices follow a conjugate prior distribution. Furthermore, the Gibbs sampling method is used to sample the posterior conditional distributions of the respective parameters, and the estimated value of the tensor is output. The missing values in the multi-measurement data are interpolated based on the estimated value of the tensor, thereby realizing data completion. In summary, the completion method of the present invention is aimed at measurement data with low measurement accuracy, high cost, and repeated measurements in some regions. The Gibbs sampling method combined with CP decomposition is used to realize data completion. Compared with the completion methods in the prior art, since the method of the present invention can utilize the information of all measurement data, it can provide a more accurate estimated value, thereby realizing more accurate data completion.

The invention discloses a security constraint unit commitment method based on tensor completion approximate dynamic programming, and the method comprises the following steps: firstly, building a security constraint unit commitment model based on a Markov decision process; secondly, decoupling the multi-period security constraint unit commitment model into a single-period sub; then, obtaining a value function and a decision function by adopting an approximate dynamic programming algorithm based on tensor completion; according to the approximate dynamic programming algorithm based on tensor completion, the approximate dynamic programming algorithm is improved from the angle of tensor completion, and all value functions and decision functions are approximately obtained by sampling a small number of state variables and decision variables. Decision is made from the angle of tensor complementation, in the face of a discrete state space, the value function of the whole state space can be approximated only by sampling a small number of state points, the calculation burden of an approximate dynamic programming algorithm in the aspect of value function approximation is effectively relieved, and the calculation efficiency of the algorithm can be greatly improved.

This invention provides a pipeline leak location method based on tensor completion, comprising: collecting leakage vibration data of a leaking pipeline using wireless accelerometers, wherein the leakage vibration data includes data sampled by N wireless accelerometers over M time periods; constructing a tensor completion model using a truncated tensor weighted norm algorithm, and constructing an optimal three-dimensional tensor structure using the firefly algorithm based on the leakage vibration data of the leaking pipeline and the tensor completion model; inputting the optimal three-dimensional tensor structure into the tensor completion model for completion; and calculating the leakage location of the leaking pipeline based on the completed three-dimensional tensor. This invention utilizes a truncated tensor weighted norm to construct a tensor completion model, which efficiently preserves effective information in the missing data, improves the tensor completion accuracy, ensures effective recovery of effective feature information from the pipeline leak data, and improves location accuracy.

The invention discloses a network digital twinning-oriented dual-view flow data sampling method and system, and mainly solves the problems of difficult key path capture and low flow reconstruction precision caused by lack of frequency domain perception of a flow sampling method in the prior art. According to the implementation scheme, historical flow interaction data in a network node sliding window are collected to form a third-order flow tensor and normalized, and node frequency domain features are extracted and then smoothly updated to obtain a smooth feature vector; mapping the vector to determine a node category, and dividing source-destination OD pair traffic categories of the whole network; calculating statistics lever scores of all OD pairs, dynamically allocating sampling budget of each category according to the statistics lever scores, and generating a sampling set and a binary sampling mask by adopting a determinacy and random combination strategy; and collecting the flow data of the corresponding position at the current moment based on the mask, constructing a tensor completion optimization model for solving completion, and outputting the recovered whole network flow data. According to the method, through double-view cooperation of the frequency domain features and the statistical lever fraction, precise coverage of a high-value traffic path is realized, and the traffic reconstruction precision of the digital twin network is effectively improved while the sampling overhead is reduced. The method can be applied to measurement and reconstruction of large-scale network traffic.

This invention belongs to the field of industrial time-series data completion technology, specifically relating to a method based on truncated L... 2,P A scalable industrial time-series data completion method based on norms includes: acquiring multi-dimensional industrial time-series data through distributed sensors; processing missing values ​​in the acquired tensors to obtain sparse tensors to be completed, and constructing a low-rank tensor completion model; integrating linear unitary transforms to enhance the scalability of the model; and defining a truncation L... 2,P Norm, based on truncated L 2,P A low-order smooth tensor completion model based on the norm is proposed. This model decomposes a complex optimization problem into multiple parallelizable subproblems to obtain the objective solution. The objective solution is then adapted to an industrial time-series data format to obtain completed industrial time-series data. This invention utilizes truncated L... 2,P The norm better approximates the tensor rank, and by integrating linear unitary transformation, it can complete high-dimensional, large-scale industrial time series data, effectively improving data quality and providing reliable support for the accurate analysis and optimization of industrial processes.

The present invention proposes a tensor completion method based on low-rank and sparse joint constraints and applies it to radar high-resolution two-dimensional sparse reconstruction imaging to enhance sparse imaging performance. The present invention realizes radar high-resolution two-dimensional imaging through the following steps: first, a tensor model of radar data is constructed by a sliding window method to capture the intrinsic structure of high-dimensional data and mine the low-rank characteristics of the data. Secondly, a tensor low-rank and sparse joint constraint model based on Kronecker basis representation is used to characterize the enhanced low-rank and sparse characteristics of the constructed radar tensor data. Then, the alternating direction multiplier method is used to efficiently solve the optimal solution of the constraint model in an iterative manner and update the relevant parameters by a closed-form solution method. The present invention verifies the superiority of the tensor completion method based on low-rank and sparse joint constraints proposed in the present invention in radar high-resolution two-dimensional imaging through comparative experiments on electromagnetic simulation and measured radar data.

The invention relates to the technical field of oil and gas exploration and development, and particularly discloses a robust mixed norm constraint high-dimensional seismic data reconstruction method and device, and the method comprises the steps: carrying out the Fourier transformation of five-dimensional data along a time axis, and obtaining the Fourier transformation data; unfolding 4D data of each frequency slice of the Fourier transform data along a mode-(m, n) to obtain a near-square matrix; a non-convex Frobenius / Nuclear mixed norm and L1 norm combined regularization constraint is applied to the near-square matrix, and a target functional of observation seismic data and reconstruction data is constructed; and alternately solving the target functional by adopting an ADMM algorithm, and carrying out inverse Fourier transform on a solving result along a frequency axis. According to the method, a high-dimensional tensor completion method under the robust Frobenius / Nuclear mixed norm constraint is introduced, accurate representation of nonlinear high-dimensional seismic data is achieved, the seismic data reconstruction and denoising precision is improved, and therefore regularized reconstruction and abnormal noise suppression of the seismic data with the low signal-to-noise ratio are achieved.

The invention belongs to the technical field of satellite remote sensing data processing and atmospheric pollution monitoring, and discloses a remote sensing image tensor completion reconstruction method and system based on space-time adaptive partitioning to solve the problems that the existing tensor completion technology is poor in partitioning adaptability and unbalanced in completion precision and convergence efficiency. The method comprises the following steps: preprocessing original remote sensing image data, and constructing a three-dimensional space-time tensor; calculating an average effective pixel proportion of a space-time region based on pixel effectiveness, dynamically matching blocks, and performing tensor block rearrangement; adaptively adjusting a low-rank threshold value and a dimension weight, and solving the low-rank tensor complementation model by adopting an economical singular value; and normalizing, weighting and fusing the block overlapping region through a weight matrix, recovering a three-dimensional space-time tensor, and expanding and outputting a complete result. According to the method, high-efficiency and high-precision space-time seamless reconstruction of the large-size remote sensing image is realized, the data complementation precision and processing efficiency in different missing modes are remarkably improved, and high-quality data support is provided for refined treatment of regional atmospheric pollution.

The invention discloses a radar signal reconstruction method based on maximum multiple correlation entropy, which adopts a maximum multiple correlation entropy criterion to define an error function, and replaces an error function based on # imgabs0 # norm commonly used in a traditional tensor completion method, and in an abnormal value pollution environment, the maximum multiple correlation entropy criterion is used to determine the error function, and the maximum multiple correlation entropy criterion is used to replace the error function based on # imgabs0 # norm. The maximum multiple correlation entropy is generally more robust than a traditional # imgabs1 # error function, and can better suppress the influence of an abnormal value, so that the radar signal reconstruction model based on the maximum multiple correlation entropy can effectively suppress the interference of the abnormal value on the system performance, thereby improving the reconstruction precision of the radar signal; therefore, accurate reconstruction and recovery of the radar signal polluted by the abnormal value are realized, the problem of poor performance caused by the influence of the abnormal value on the reconstruction of the radar signal at present is solved, and the signal processing capability of the system in a complex electromagnetic environment is further improved.

The present application relates to the technical field of electric energy scheduling, and particularly relates to an electric energy scheduling method based on an energy storage system.The method comprises the following steps: deploying a distributed sensing array inside a battery module to collect multi-source heterogeneous data, and performing standardization processing to generate a standardized energy storage dataset; performing tensor completion on the standardized energy storage dataset to generate energy storage tensor completion data; performing time-space dimension analysis on the energy storage tensor completion data, and performing cross-modal feature fusion to generate an energy storage cross-modal feature space;Therefore, through the structured processing of multi-dimensional fused data and the dynamic feedback optimization of the digital twin model, the present application solves the problems of lagging prediction and poor model adaptability in traditional cold-chain warehouse environments, and improves the accuracy of temperature prediction and the intelligent level of warehouse management.

The present application relates to the technical field of fault diagnosis, in particular to a power grid fault analysis method and system, which comprises constructing a fusion tensor and performing dimension reduction processing on the fusion tensor to generate a fusion feature matrix; constructing a convolutional autoencoder model to detect abnormal fusion feature matrix; classifying power grid faults according to abnormal data of the abnormal feature matrix, and calculating power grid equipment fault area based on the result of power grid fault classification. The present application has the beneficial effect of ensuring the integrity and accuracy of data by using a high-precision low-rank tensor completion algorithm to complete power grid operation data. Key features are extracted from multi-dimensional data collected by multiple source sensors and a fusion feature matrix is constructed using Tucker decomposition and matrix compression technology, effectively improving the efficiency of data processing and the ability of feature expression. The application of the convolutional autoencoder model enhances the detection accuracy of abnormal features, and the improved weighted summation and hierarchical clustering-based method improves the accuracy of fault classification and positioning.

The application discloses a track tensor completion and abnormality repairing method based on singular value weighting and truncation, and relates to the technical field of flight track data processing.The application fully utilizes easily-obtained ADS-B track data, and mines the internal law through intelligent learning capability; meanwhile, aiming at the complex problem that missing and abnormality are coupled with each other in the track data, a robust tensor model is innovatively constructed, which fuses an adaptive weight mechanism, singular value truncation and sparse abnormality constraint, so that the collaborative and accurate processing of missing completion and abnormality repairing is realized; further, an optimization solving strategy based on an alternating direction multiplier method is proposed, and through the augmented Lagrange method, low-rank track tensors, sparse abnormality tensors and auxiliary variables are efficiently block-iteratively optimized, so that the complete track can be accurately recovered in a complex data environment.

The application discloses an ICCEEMDAN-LRTC-based power load completion method and system and belongs to the field of power equipment state monitoring. The improved adaptive noise complete ensemble empirical mode decomposition is performed on the power load time series data containing defects, and the non-stationary signal of the load is decomposed into different IMF sub-sequences; a limited number of IMF sub-sequences are reconstructed into a tensor array according to a time period, and a low-rank tensor completion algorithm is used to complete the missing values of the power load tensor containing defects. The method can fully consider the internal relationship of multidimensional data, so that the missing data can be more comprehensively completed. Finally, the completed IMF sub-sequences are reconstructed to obtain complete load time series data.

The invention relates to the technical field of data calibration, and discloses a geophysical survey data feedback calibration method, which is characterized in that an initial model is established through geological data integration, and a low-resolution region is calculated and identified by using a sensitivity matrix. A multidirectional measuring line array is deployed in an acquisition stage, and the position of a seismic source is dynamically adjusted by monitoring an offset distance distribution heat map in real time. And when frequency spectrum missing is detected, low-frequency seismic source supplementary excitation is automatically triggered. And for the acquisition data missing region, a low-rank tensor completion algorithm is adopted to recover a complete data volume. In the inversion process, long-offset data are preferentially utilized to construct a macroscopic velocity model, and then short-offset reflected wave data are gradually introduced to refine a local structure. And the model uncertainty analysis module automatically identifies a low-confidence region, generates supplementary acquisition coordinates and drives field equipment to implement directional encryption, newly added data is reinjected into an inversion process after being quickly processed, and a continuously optimized closed-loop system is formed.

The present invention discloses a visual data reconstruction method and system based on direction-aware tensor nuclear norm, which represents visual data as a multi-order tensor x; obtains the direction-aware tensor nuclear norm of the visual data, and establishes a low-rank tensor completion model based on the direction-aware tensor nuclear norm; optimizes the low-rank tensor completion model through an optimization algorithm to obtain complete reconstructed tensor data. The present invention proposes a new direction-aware tensor nuclear norm, which reconciles the influence of directions by transforming all modes of the tensor, and realizes the tensor integrity reconstruction of high-dimensional data through an efficient iterative algorithm.

This invention relates to the field of data calibration technology and discloses a geophysical survey data feedback calibration method. An initial model is established through geological data integration, and low-resolution areas are identified using sensitivity matrix calculations. During the acquisition phase, a multi-azimuth survey line array is deployed, and the source position is dynamically adjusted by monitoring the offset distribution heatmap in real time. When a spectral gap is detected, low-frequency source supplementary excitation is automatically triggered. For areas with missing acquired data, a low-rank tensor completion algorithm is used to recover the complete data volume. During the inversion process, long-offset data is prioritized to construct a macroscopic velocity model, and then short-offset reflected wave data is gradually introduced to refine the local structure. The model uncertainty analysis module automatically identifies low-confidence areas, generates supplementary acquisition coordinates, and drives field equipment to implement directional encryption. The newly added data is rapidly processed and reinjected into the inversion process, forming a continuously optimized closed-loop system.

The invention relates to the technical field of power grid risk assessment, and discloses a power grid risk assessment method based on integrated load flow calculation and topology analysis, and the method comprises the steps: collecting and preprocessing power grid operation data, and organizing the preprocessed data into a four-dimensional high-order tensor structure; constructing a power grid topological relation tensor network; constructing an optimization problem through an L1 norm form, and solving the optimization problem by using an augmented Lagrangian multiplier method; the complex coupling relation among time, space and parameters is revealed by analyzing the relation among different dimension factor matrixes; performing risk assessment on different granularity levels under a multi-scale tensor analysis framework, and coordinating analysis results of each level to form comprehensive assessment; designing a tensor completion estimation algorithm, constructing an optimization model by using low-rank hypothesis, and solving a complete data set through a tensor low-rank decomposition method; according to the method, the high-order tensor representation and tensor network decomposition technology is adopted, so that the dimension and complexity of data are effectively reduced, and the calculation complexity is reduced.

The embodiment of the application discloses a structural color design method and device based on a tensor completion algorithm, which comprises the following steps: obtaining spectral data and geometric data of a dielectric array constituting a structural color, and combining the spectral data and the geometric data into multi-dimensional tensor data, wherein the tensor data comprises a to-be-completed tensor and a complete tensor containing all known entries; applying an alternating least squares method to Tucker decomposition of the to-be-completed tensor according to the tensor data, so as to obtain a minimum rank tensor after tensor completion, which is the same as known entries in the to-be-completed tensor; and converting the minimum rank tensor into spectral data and geometric data to obtain corresponding structural color. Through the above method, the embodiment of the application can quickly and accurately design structural color, avoids complex electromagnetic theory, and improves the device design efficiency of a small number of features.