39 results about "Alternating least squares" patented technology

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention relates to the technical field of servo control, in particular to a servo driving current harmonic compensation method and system for electromagnetic interference suppression. The method comprises the following steps: extracting common-mode and differential-mode interference by using current and voltage sampling and combining differential measurement, and configuring a multi-stage passive filter according to spectrum characteristics to construct a suppression channel; establishing a digital interference cancellation mechanism, generating a reverse compensation signal by adopting self-adaptive trapped wave and a DSP (Digital Signal Processor), and injecting the reverse compensation signal into a control loop to form a software-defined multi-channel suppression architecture; after interference suppression, a high-order tensor model is constructed for the purified three-phase current, and an alternating least square method is adopted for decomposition so as to separate multi-band harmonic waves; a graph neural network is introduced, harmonic coupling is represented, and dominant and residual harmonic decoupling is realized through message passing; according to a grading progressive strategy, dominant harmonic waves are firstly subjected to coarse compensation, then residual harmonic waves are finely adjusted, and a compensation sequence is adaptively optimized through iterative feedback, so that high-robustness closed-loop control of servo driving is realized.

The invention discloses a model-free state estimation method and system for a power distribution network based on canonical multi-linear decomposition and a storage medium in the field of state estimation of a power system, and aims to solve the technical problem that the power distribution network cannot execute state estimation under the condition that real-time topological information of the power distribution network is lost. The method comprises the following steps: acquiring historical measurement data of a power distribution network and preprocessing the historical measurement data, constructing a three-dimensional tensor according to a time dimension, a node dimension and a measurement type dimension, and performing tensor decomposition on the three-dimensional tensor through an alternating least square algorithm to obtain an optimal decomposition factor matrix, and reconstructing a measurement tensor by using an optimal decomposition factor matrix to realize filtering of historical measurement data in an original three-dimensional tensor and complementation of missing measurement data, and finally extracting a voltage sub-tensor in the reconstructed measurement tensor and performing reverse normalization processing to obtain a voltage amplitude estimated value. The method is applied to state estimation of the power distribution network, and stable and accurate state estimation calculation can be achieved under the condition that topological information is not used.

The invention belongs to the technical field of radar parameter processing, and discloses a polarization MIMO multi-parameter estimation method and system based on emission polarization modulation. The polarized multiple-input multiple-output high-frequency ground wave radar array structure is composed of a transmitting end and a receiving end; the transmitting end performs transmitting polarization modulation on a target signal transmitted by the transmitting array element through the polarization phase controller; a receiving end receives the echo data of the target after emission polarization modulation, and echo data reconstruction is carried out after pulse pressure processing is carried out on the echo data; constructing a three-order tensor form, and performing tensor decomposition on the three-order tensor form through an alternating least square method; polarization parameter joint estimation is carried out by using a multi-parameter joint estimation signal processing method. The invention aims at designing a novel high-frequency ground wave radar transmitting and receiving array structure and a P-MIMO-HFSWR signal processing method, and multi-parameter high-precision estimation of a sea surface target is realized.

The application discloses a deep learning driven target parameter estimation method, and relates to the cross field of radar signal processing and artificial intelligence. The application embeds the maximum correlation entropy criterion into an alternating least square process, expands the alternating least square process into a differentiable network layer containing weight updating and a damped weighted least square, realizes end-to-end optimization under a unified criterion by combining a deep initialization network, introduces a sigma-Net based on a teacher-student framework, dynamically predicts an optimal kernel width based on three types of characteristic features, namely, a residual tail ratio, a peak ratio and an estimated GSNR, and synchronously optimizes initialization quality and kernel width strategies through double-path gradient back propagation, so that the synergistic gain of high-quality initialization, robust updating and adaptive parameter adjustment is realized.

The present invention provides a method for accelerating the calculation of a recommendation system based on an analog in-memory computing circuit, and belongs to the fields of semiconductor, analog computing, and integrated circuit technology. Based on the iterative calculation of the alternating least squares method, the method of the present invention designs a block matrix method, utilizes a variable resistor array to solve large-scale matrix decomposition problems, and uses analog computing to accelerate the large number of ridge regression calculations contained in the block matrix method, thereby realizing analog computing acceleration of the recommendation system. The method of the present invention can achieve high-speed and energy-efficient matrix decomposition of the recommendation system, providing a new solution for hardware acceleration of the recommendation system training process in the context of big data, and has broad application prospects.

The invention relates to a railway communication interference elimination algorithm, and aims to realize interference elimination and symbol extraction. The algorithm comprises the following steps of: 1, randomly initializing # imgabs0 #; 2, performing tensor decomposition by using a multivariate decomposition interference elimination algorithm; 3, solving a rank-one matrix decomposition factor {h1, a1, s1} through a regular multivariate alternating least square algorithm of blind decomposition; and 4, estimating user information by using a singular value decomposition algorithm. The multivariate decomposition interference elimination algorithm comprises the following steps: step 1, obtaining a signal tensor # imgabs1 # through interference cancellation; step 2, decomposing a rank-one matrix factor {h1, a1, s1} by using a regular multivariate alternating least square algorithm of blind decomposition; step 3, reconstructing a signal # imgabs2 # and calculating a residual error # imgabs3 #; step 4, combining a rank 1 matrix factor of # imgabs4 # and a rank 1 matrix factor of # imgabs5 # to obtain a rank 2 initialization matrix factor H * = [h1, h '] and A * = [a1, a'], and # imgabs6 #; step 5, decomposing the rank matrix factors {Hr, Ar, Sr} by adopting a regular multiple alternating least square algorithm of blind decomposition, and finally reconstructing the signal and extracting information.

The invention discloses a recommendation method based on interpretable generalized logic transformation matrix decomposition. The recommendation method comprises the following steps: converting an original scoring matrix into normal distribution data through a generalized logic transformation function; constructing indexes based on similarity and indexes based on ranking; calculating probability distribution and expected scores of scores of the recommended items by the similar users, and generating interpretability indexes in combination with the similarity indexes; integrating the interpretability index into a matrix decomposition objective function for optimization; solving a user feature matrix and a project feature matrix through an alternating least square method or stochastic gradient descent; calculating a prediction score and mapping the prediction score back to an original score interval through generalized logic inverse transformation; generating a recommendation list; the method provided by the invention has wider applicability and higher performance in practical application.

The present application relates to a kind of millimeter wave MIMO-OFDM system channel estimation enhancement method, focus on solving the prior information of stable path that existing method cannot effectively utilize, leading to unnecessary calculation overhead and precision loss problem in estimation process.Its implementation steps are:1) for each subcarrier, base station (BS) is equipped with different beamforming vector in continuous time frame, user (UE) independently detects transmission signal in each time subframe;2) separate known path information from received signal, obtain the tensor slice of prior and unknown information joint, based on parallel factor (PARAFAC) model it is constructed as three-order tensor;3) using prior information optimization alternating least squares (ALS) algorithm, estimate three factor matrices, then use maximum likelihood (ML) estimation to obtain unknown channel parameters.The channel estimation enhancement method proposed in the present application can optimize ALS algorithm using the prior information of stable path, can reduce estimation error, improve system performance.

The present application relates to resource recommendation technical field, especially to a kind of learning resource recommendation method and system based on user portrait, including establishing user portrait matrix and learning resource feature matrix, and using factor decomposition machine and collaborative filtering algorithm to build the implicit semantic matching model of user and learning resource;The parameter of implicit semantic matching model is learned by alternating least squares method, and user-learning resource implicit semantic association feature is generated;User-learning resource implicit semantic association feature is used as individual code, and the Pareto optimal recommendation solution set of multi-objective learning resource recommendation optimization model is searched using non-dominated sorting genetic algorithm, and learning resource recommendation scheme is generated in combination with optimal recommendation decision model.The present application dynamically adjusts recommendation strategy by combining multi-objective optimization and deep reinforcement learning etc., improves the accuracy of recommendation, while it can balance the diversity and personalization of recommended content, improve the dynamic adaptability and robustness of recommendation system.

The application discloses a spatial weighted pooling shift invariant rank-(L, L, 1, 1) block term decomposition algorithm suitable for multi-subject fMRI data, and belongs to the field of medical signal processing. On the basis of an alternating least squares (ALS) method, a spatial weighted pooling processing method is proposed to preprocess multi-subject fMRI data, down-sample and smooth fMRI data images, and significantly reduce the fMRI data volume and remove most of the noise. In addition, considering the high space-time difference between subjects, the method combines spatial orthogonalization constraints and time shift invariance, relaxes and compresses the rank-(L, L, 1, 1) BTD model of the fMRI data, and improves the separation performance of the algorithm.

The present invention provides a WIFI parameter estimation and positioning method assisted by multiple RISs. Aiming at the indoor scenario with complex multipath effects, the present invention first constructs the received signal assisted by multiple RISs into a fourth-order parallel factor model based on tensors; secondly, utilizes subcarrier resources and adopts virtual antenna array technology to rearrange the model to meet the uniqueness of tensor decomposition; subsequently, uses the four-linear alternating least squares algorithm to decompose the rearranged tensor model, and extracts multipath channel parameters by using subspace methods; finally, adopts a search-free positioning method to achieve accurate positioning of users and multiple RIS nodes. Therefore, the indoor joint estimation and positioning based on the rearranged tensor assisted by multiple RISs proposed by the present invention has the advantage of high accuracy, and at the same time has stronger robustness compared with existing competing methods, thus better meeting the requirements of actual communication systems.

The invention discloses a method for measuring the attitude of a separated electromagnetic vector sensor array platform of a reference wave structure, belongs to the technical field of navigation, and is suitable for measuring the attitude of an aircraft-borne array aerospace platform. The electromagnetic vector sensor is separated in space, engineering realization is facilitated, cross polarization is avoided, and attitude measurement precision is improved. And according to the change rule between the attitude position of the airborne electromagnetic vector sensor and the received signal, an array steering vector of the airborne electromagnetic vector sensor is established. According to a relationship between a three-dimensional information structure of signal time, polarization and space-frequency phase delay and an attitude, signal data is received by arranging all electromagnetic vector sensors, and a parallel factor (PARAFAC) trilinear data model can be obtained. By popularizing the traditional parallel factor alternating least square algorithm, the platform attitude is calculated with high precision under the orthogonal constraint condition when the separated electromagnetic vector sensor is possibly incomplete.

The application belongs to the technical field of radar parameter processing, and discloses a polarization MIMO multi-parameter estimation method and system based on transmission polarization modulation. A polarization multi-input multi-output high-frequency ground wave radar array structure is formed by a transmission end and a receiving end. The transmission end performs transmission polarization modulation on the target signal transmitted by the transmission array element through a polarization phase controller. The receiving end receives the echo data of the target after the transmission polarization modulation, performs pulse compression processing on the echo data, and then reconstructs the echo data. The form of a third-order tensor is constructed, and then the tensor decomposition of the third-order tensor form is performed through an alternating least square method. The polarization parameter joint estimation is performed through a multi-parameter joint estimation signal processing method. The application aims to design a novel high-frequency ground wave radar transmission-receiving array structure and a P-MIMO-HFSWR signal processing method, and realize the multi-parameter high-precision estimation of the sea surface target.

The invention relates to the technical field of computer data processing, provides a business-driven data asset unified management method and system, and solves the technical problems of lagging data asset discovery and low management efficiency. The method comprises the following steps: acquiring a database access log and business metadata; analyzing the database access log, and constructing a sparse tensor according to the screened triple; carrying out dimension reduction decomposition operation on the sparse tensor by utilizing an alternating least square method to obtain a user potential vector, a data potential vector and a time potential vector; taking a data potential vector corresponding to the unaccessed data in the vectors as an input vector, and inputting the input vector into the trained neural collaborative filtering model to obtain a prediction preference value of the unaccessed data; according to the method, the unaccessed data are arranged in a descending order according to the predicted preference values, and potential target data are identified based on the predicted preference values, so that unified management of the data assets is realized, and intelligent active discovery and efficient unified management of the data assets are realized.

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.

The invention relates to the technical field of resource recommendation, in particular to a learning resource recommendation method and system based on a user portrait, and the method comprises the steps: building a user portrait matrix and a learning resource feature matrix, and constructing a latent semantic matching model of a user and a learning resource through a factorization machine and a collaborative filtering algorithm; learning parameters of the latent semantic matching model through an alternating least square method, and generating user-learning resource latent semantic association features; and taking the user-learning resource latent semantic association features as individual codes, searching a Pareto optimal recommendation solution set of the multi-target learning resource recommendation optimization model by using a non-dominated sorting genetic algorithm, and generating a learning resource recommendation scheme in combination with an optimal recommendation decision model. According to the method, the recommendation strategy is dynamically adjusted by combining technologies such as multi-objective optimization and deep reinforcement learning, the recommendation accuracy is improved, meanwhile, the diversity and personalization of recommended contents can be balanced, and the dynamic adaptability and robustness of a recommendation system are improved.

The invention relates to a smoked mural pigment spectral unmixing method, and belongs to the field of hyperspectral unmixing. The method comprises the following steps: S1, establishing a pure pigment spectrum library and a smoking spectrum library as a data basis, S2, constructing a sparse unmixing model based on end member linear combination of the smoking spectrum library, S3, introducing a spatial self-adaptive mechanism based on spectrum similarity measurement, and dynamically adjusting regularization weight through abundance gradient analysis to optimize an objective function. And S4, implementing layered iterative optimization by adopting an alternating least square method in combination with spectral smoothing constraint, and finally realizing accurate unmixing of smoke pigment components. According to the method, smoke layer interference and a mixed pixel nonlinear effect are effectively overcome through dynamic weight adjustment, and spectrum similarity analysis and abundance space distribution characteristics are fused. The method is mainly applied to quantitative analysis of original pigment components under a smoked covering layer in ancient murals, and provides accurate material composition data support for cultural relic restoration.

This invention relates to a method for simultaneous localization and mapping (SLAM) assisted by a UAV equipped with a reconfigurable smart surface (UAV-RIS), addressing the problems of parameter decoupling difficulties caused by cascaded channels and large amplitude-based localization and mapping errors due to signal strength distortion. The method includes: 1) constructing a nested parallel factor model of the received signal and performing tensor decomposition using a bilinear alternating least squares method to achieve joint channel parameter estimation and signal detection; 2) initializing and removing outliers using an amplitude-independent geometric inference method based on the estimated geometric parameters; and 3) optimizing the model using a weighted least squares method to determine user state and environmental characteristic parameters. This method reduces the impact of amplitude model mismatch and improves parameter estimation accuracy in complex channel environments, making it suitable for autonomous localization and mapping in low-altitude wireless networks.

The application discloses a kind of intelligent reflecting surface auxiliary cascading surface array channel nested tensor decomposition estimation methods, first constructs cascading channel model, initialization each end guiding matrix and channel matrix in turn;Zero intelligent reflecting surface phase shift is first placed, respectively constructs the tensor model of transceiver end parallel factor decomposition, combined with three linear alternating least square method and least square method, completes two-dimensional angle estimation of transceiver end;Again control phase shift, through two linear alternating least square method, Khatri-Rao decomposition, complete intelligent reflecting surface two-way angle estimation, finally rely on Kronecker product and Khatri-Rao product model solves beam gain.The application relies on nested parallel factor decomposition, realizes four angles automatic pairing, adapts single, multi-beam scene, reduces high-dimensional calculation amount, suppresses off-grid energy leakage, without signal autocorrelation matrix and complex angle pairing, adapts super large scale surface array, improves channel estimation applicability, supports sensing integration system design.

The application discloses a recommendation method based on an interpretable generalized logistic transformation matrix decomposition, and comprises the following steps: converting an original score matrix into normally distributed data through a generalized logistic transformation function; constructing a similarity-based index and a ranking-based index; calculating a probability distribution of a similar user's score on a recommended item and an expected score, and combining the similarity index to generate an interpretability index; integrating the interpretability index into a matrix decomposition target function for optimization; solving a user feature matrix and an item feature matrix through an alternating least squares method or a stochastic gradient descent; calculating a predicted score and mapping back to an original score interval through a generalized logistic inverse transformation; and generating a recommendation list; the application has wider applicability and higher performance in practical application.

This invention provides a deep denoising-based multi-RIS indoor positioning method. For indoor scenarios with multipath interference and noise, this invention first constructs a fourth-order tensor model from the channel state information reflected from multiple indoor RIS points and scattering points to the receiver. Second, it uses antenna rearrangement technology to rearrange the tensor model to satisfy the decomposition uniqueness condition. Then, it utilizes the denoising capabilities of a deep learning architecture to suppress noise in the received signal. Further, it employs an optimized quadlinear alternating least squares algorithm to estimate the factor matrix and extract channel parameters. Finally, it uses a geometrically based search-free spatial positioning method to estimate the user's location and azimuth angle, and map the indoor environment. Therefore, the deep denoising-based multi-RIS indoor positioning method proposed in this invention has higher accuracy and robustness compared to comparative algorithms, and is more in line with the needs of practical communication scenarios.

The present invention discloses a harmonic and interharmonic frequency estimation method based on parallel factor compressed sensing, comprising the following steps: (1) constructing a sensor receiving signal into a parallel factor trilinear model; (2) compressing the three-dimensional matrix of the received signal according to the compressed sensing theory; (3) using a trilinear alternating least squares algorithm to perform cyclic iterative decomposition on the compressed model until convergence to obtain an estimate of the parameter matrix; (4) constructing a sparse recovery problem in frequency estimation, and sequentially using an orthogonal matching pursuit method to obtain frequency estimates. The present invention is applicable to harmonic and interharmonic signals in power systems and is easy to implement in actual use; the algorithm of the present invention has a small amount of calculation and has a low capacity requirement for data storage; the algorithm of the present invention can obtain paired frequency estimates with the help of a parallel factor model without the need for additional pairing.

This invention provides a terahertz massive MIMO wireless simultaneous localization and mapping method to address the difficulties in decoupling multidimensional parameters due to the dual-wideband effect and the data association ambiguity caused by the lack of Doppler information in dynamic scenarios. The implementation steps are as follows: 1) Construct a third-order tensor model of the received signal containing spatial, frequency, and time dimensions, and expand the tensor using a time smoothing strategy to overcome the rank deficiency problem; 2) Iteratively decompose the observation tensor in the compressed domain using a trilinear alternating least squares algorithm to achieve high-precision channel parameter decoupling and communication signal detection; 3) Encapsulate the estimated Doppler, angle, and time delay into measurement vectors, calculate the joint posterior probability based on the confidence propagation algorithm, eliminate data association ambiguity, and infer the trajectory of the mobile agent and environmental characteristics. This invention effectively overcomes the dual-wideband effect, achieving accurate tracking and robust mapping.

The invention discloses a high-precision time difference estimation method combining cross-spectrum and trilinear decomposition. First, L monitoring nodes are used to receive radiation signals, and the cross-power spectrum of the signals received by all nodes and the reference node is calculated; secondly, non-zero frequency band signals are extracted, and the frequency domain cross-spectrum data between each monitoring node and the reference node are integrated to construct a trilinear model; then, a parallel factor method is used to perform decomposition iteration through the trilinear alternating least squares method to obtain a frequency domain time difference estimation matrix; finally, a normalization method is used to eliminate scale ambiguity, and the time difference matrix is ​​analyzed to obtain a high-precision time difference estimation. The invention can break through the limitation of sampling frequency and obtain a time difference estimation with a higher accuracy than the sampling interval, thereby effectively improving the positioning accuracy.

The application provides a platform attitude tracking PARAFAC decomposition measurement method of a reference wave structure, and comprises the following steps: establishing an array direction vector of an airborne electromagnetic vector sensor according to the change rule between the attitude position of the airborne electromagnetic vector sensor and a received signal; arranging all electromagnetic vector sensor received signal data to obtain a parallel factor three-linear data model according to the relationship between the signal time, polarization, three-dimensional information structure and attitude; the observation matrix is windowed, a tracking algorithm is used to replace the traditional algorithm, and the complexity increase with time is avoided; the traditional parallel factor alternating least square algorithm is extended, the platform attitude data of the reference wave structure is tracked under the orthogonal constraint condition, and the platform attitude is solved.

The present invention provides a process knowledge recommendation method, system, electronic device and storage medium, belonging to the field of knowledge recommendation. The process knowledge recommendation method includes: obtaining the domain-role vectors of each user and the category vectors of each knowledge item, and calculating the demand values of each user for each knowledge item to obtain a user-knowledge demand matrix; determining an initial user-knowledge score matrix according to the historical knowledge behavior data of the user; performing matrix decomposition and score prediction on the initial user-knowledge score matrix by using a matrix decomposition model and the alternating least squares method to obtain a user-knowledge score prediction matrix; performing weighted summation on the user-knowledge demand matrix and the user-knowledge score prediction matrix to obtain a user-knowledge hybrid matrix; sorting the final scores of each knowledge item in the user-knowledge hybrid matrix, and recommending to the user the knowledge items whose final scores are greater than a set threshold. It can recommend the most appropriate process knowledge to the user in real time, efficiently and accurately.

The invention provides a management scheduling method for a proxy IP pool, and relates to the technical field of proxy IP management. Comprising the following steps: S1, according to historical response time between a proxy IP and a target address, constructing a scoring matrix of the proxy IP to the target address; s2, approximately expressing the score matrix as a product of a proxy IP feature matrix and a target address feature matrix; s3, introducing a regularization item on the basis of the square loss function, and defining an optimization objective function; s4, solving the optimization objective function by adopting an alternating least square method; s5, accumulating scores of all the target addresses by each proxy IP, and normalizing the scores to obtain a comprehensive score of the corresponding proxy IP; and S6, scheduling the proxy IPs according to the sequence of the comprehensive scores from large to small. According to the method, the proxy IP can be dynamically scored for measuring the availability of the current proxy IP in the IP pool resource, so that the scheduling priority of the proxy IP is determined, and the overall service quality and reliability are improved.

The present application relates to a method for spectral unmixing of smoked mural pigments, belonging to the field of hyperspectral unmixing. The method establishes a pure pigment spectral library and a smoked spectral library as data basis through S1, constructs a sparse unmixing model based on the linear combination of endmembers of the smoked spectral library through S2, introduces a spatial adaptive mechanism based on spectral similarity measurement through S3, dynamically adjusts the regularization weight to optimize the objective function through abundance gradient analysis, and finally realizes the accurate unmixing of smoked pigment components through S4 by adopting the alternating least squares method combined with spectral smoothing constraint to implement hierarchical iterative optimization. The method effectively overcomes the interference of smoked layers and the nonlinear effect of mixed pixels through dynamic weight adjustment, and integrates spectral similarity analysis and abundance spatial distribution characteristics. It is mainly applied to the quantitative analysis of the original pigment components under the smoked covering layer in ancient murals, and provides accurate material composition data support for cultural relic restoration.

The application relates to the technical field of computer data processing, and provides a data asset unification management method and system based on business driving, which solves the technical problems of data asset discovery lag and low management efficiency. The method comprises the following steps: acquiring database access logs and business metadata; analyzing the database access logs, and constructing a sparse tensor according to the screened triplets; performing dimension reduction decomposition operation on the sparse tensor by using an alternating least square method to obtain user potential vectors, data potential vectors and time potential vectors; taking the data potential vectors corresponding to the unvisited data in the vectors as input vectors, inputting the input vectors into a trained neural collaborative filtering model, and obtaining the predicted preference values of the unvisited data; arranging the unvisited data in descending order according to the predicted preference values, and identifying potential target data based on the predicted preference values, so as to realize the unified management of data assets, and realize intelligent active discovery and efficient unified management of the data assets.