164 results about "Data Matrix" patented technology

This invention relates to a method and apparatus for detecting and correcting federated learning data. The method includes: acquiring intermediate data uploaded by each participating party, the intermediate data including location information of data to be supplemented, suspicious data, and indicators related to the suspicious data; performing a global analysis based on the indicators related to the suspicious data to filter the suspicious data and determine the data to be corrected; determining a problem data matrix based on the location information of the data to be supplemented and the data to be corrected; acquiring the associated data corresponding to each problem data in the problem data matrix from each participating party, analyzing the associated data, and determining a correction matrix for each participating party, so as to provide data processing services based on the correction matrix of the participating parties and the local data of the participating parties. The technical solution provided by this invention can effectively solve the technical problems of data accuracy, consistency, and security by combining data quality, data validity detection, and data protection mechanisms.

A vector multiply-accumulate processing method and device, a storage medium and a program product, the method comprising: in response to receiving a first multiply-accumulate instruction, the first multiply-accumulate instruction comprising a first register, a second register, a target register and a first coefficient z, selecting (z) elements from an update coefficient vector as current update coefficients according to the first coefficient z, and copying each current update coefficient to (z) respectively, wherein (z) and (z) are both preset functions of the first coefficient z, and the values of (z) and (z) are both greater than or equal to 1, the first register is used to store the update coefficient vector, the second register is used to store a first data matrix, the target register is used to store a second data matrix, and the first data matrix and the second data matrix have the same number of columns; multiplying (z)*(z) current update coefficients with elements in (z) rows and (z) columns in the second register respectively, and adding the multiplication results with elements in corresponding positions of the target register to obtain a vector multiply-accumulate result.

The application provides a feature region identification method and system based on an atomic force microscope (AFM) topography, and the method comprises the following steps: acquiring an AFM topography data matrix of a nano preparation region; dividing the AFM topography data matrix into a plurality of sub-regions corresponding to preparation structures according to the number of the preparation structures; performing a region positioning step on each sub-region to determine the start and end ranges of a feature region representing a nano structure in a vertical direction in each sub-region; performing a center line fitting step in the start and end ranges of the feature region, fitting feature points in multiple rows of data to obtain a feature center line representing an extension direction of the nano structure; and performing a row-by-row identification operation on multiple rows of topography data in the feature region along the feature center line to identify feature region boundary points in each row of data. The technical scheme provided by the application realizes batch, automatic and high-precision identification of a plurality of nano structure feature regions in a complex AFM topography.

The present application belongs to the technical field of PCB defect detection, and particularly relates to a PCB back-drilling defect detection method, a detection device and an evaluation method. The method first identifies and preliminarily screens the holes to be rechecked through the two-dimensional image of the whole board; then performs three-dimensional topography scanning on the holes to be rechecked, and performs spatial registration on the two-dimensional image subgraph and the three-dimensional point cloud data matrix of the same hole through a unified coordinate system, so that the two-dimensional texture and the three-dimensional topography information are accurately corresponded; based on the fused data after registration, residual stubs, copper wire residues, dirt, microcavities, depressions, scratches and hole wall roughness are identified and classified. The present application corresponds the two-dimensional preliminary screening result and the three-dimensional measurement data through spatial registration, so that the copper wire and the dirt, the microcavity and the scratch can be distinguished based on the three-dimensional topography difference, and the size parameters such as the residual stub length and the hole wall roughness are included in the quality evaluation.

This invention relates to the field of radar target detection technology, and provides a robust method and apparatus for radar extended target detection under weighted generalized inverse Gaussian clutter. The invention uses a clutter prior information vector and a moment estimation order vector to estimate the clutter parameter vector; based on the training sample matrix, the data matrix to be detected, and the estimated values ​​of the clutter parameter vector, it estimates the inverse expectation of the texture component; based on the training sample matrix, the data matrix to be detected, the signal steering vector, and the estimated values ​​of the inverse expectation of the texture component, it constructs a test statistic; and based on the test statistic, it determines the target state. This invention solves the problems of radar extended target detection failing to meet real-time processing requirements and exhibiting poor robustness.

The application discloses an AI model training optimization method and system, relates to the technical field of model training optimization, and comprises the following steps: collecting real-time running data in an AI model training process, and generating a multidimensional real-time running data matrix; performing outlier elimination on the real-time running data matrix, and generating a standardized running data stream; extracting characteristic parameters and coding into a training state characteristic vector; performing multidimensional evaluation on the training state characteristic vector, and generating an original optimization score; generating a comprehensive optimization suggestion; generating hierarchical optimization content, and converting into a personalized optimization report; and forming a training process self-adaptive adjustment cycle based on optimization result feedback. The application solves the problems of low optimization efficiency and difficulty in accurate positioning caused by manual tuning depending on artificial experience, improves resource utilization and optimization accuracy of the AI model training, and realizes intelligent dynamic adjustment of the training process through a closed-loop self-adaptive control mechanism.

A method for detecting the cliques of evaluators in a decision-making process includes collecting a data matrix Sij to a computer system using an automatic input interface. The elements of the matrix Sij are the real numbers in a predefined range. Each of the elements of the matrix Sij represent a numerical evaluation provided by an evaluator j for an evaluated entity i. For each pair of evaluators j, where j1 and j2 (j1≠j2) from the data matrix Sij, calculating by the computer system the Pairwise Adjusted Distances EDj<sub2>1< / sub2>j<sub2>2< / sub2>. Applying by the computer system a nonlinear transformation to the Pairwise Adjusted Distances EDj<sub2>1< / sub2>j<sub2>2< / sub2>. Identifying cliques of evaluators by comparing by the computer system the transformed distancesEDj1⁢j2*to a robust lower threshold.

This invention discloses a method and system for extracting meta-analysis literature based on graph-anchored multi-agent systems. The method includes reconstructing unstructured PDFs into a hierarchical semantic document structure through visual document analysis and identifying functional area labels; applying functional area anchoring constraints to limit information search to specified functional area nodes; scheduling a multi-agent collaborative extraction network consisting of a reconnaissance agent, a logic assembly agent, and an indicator retrieval agent; and completing experimental variable identification, logical mapping between treatment and control groups, and cross-paragraph indicator retrieval through structured message dialogue and multi-round collaboration; employing a neural symbolic hybrid engine, where semantics are parsed by a large language model and precise mathematical aggregation operations and dimensional conversions are performed by a deterministic symbolic computation engine; and generating a structured data matrix with inverted index pointers. This application achieves high-precision, zero-error, and traceable automated extraction of meta-analysis literature data.

The application provides a bearing temperature and vibration signal fault evolution path intelligent identification method and system, relates to the technical field of fault identification, and comprises the following steps: collecting and time-aligning bearing temperature, vibration signals and working condition parameters to construct a multi-source heterogeneous data matrix; extracting multi-dimensional features after adaptive noise reduction processing; constructing a fault evolution mechanism causal correlation network by using a causal inference model; performing time series clustering to divide health state clusters based on the network and establishing a state transition model by using a hidden Markov model; and finally applying a Viterbi algorithm to decoding to obtain an optimal fault evolution path. The method can effectively identify the bearing fault evolution law and improve the prediction accuracy.

The application relates to the technical field of transformer monitoring, and discloses a main transformer multi-dimensional state online monitoring method based on big data analysis, which comprises the following steps: mapping a transformer body into a knowledge graph node, dynamically calculating an edge weight, obtaining a dynamic knowledge graph, combining the dynamic knowledge graph with a three-dimensional data matrix, generating an evolution rule set and a parameter threshold, reasoning on the basis of the evolution rule set, and outputting a rule priority. Through multi-dimensional data fusion and dynamic rule evolution, the application realizes high self-adaptation and accurate abnormality detection of main transformer state monitoring, and the core advantage lies in a flexible architecture which is not model-driven, so that the detection logic can be dynamically adjusted through a rule self-evolution system without relying on a fixed model, equipment aging and working condition evolution are adapted to, a full-parameter sensor network covers four core areas, working condition information is integrated, a three-dimensional data matrix is formed, and comprehensive data support is provided for abnormality detection.

The application provides a long-short-term combined reinforcement learning charging load prediction method and system, and belongs to the technical field of intelligent power distribution network optimal operation. The method comprises the following steps: acquiring multi-dimensional charging feature data of multiple charging stations in a region at continuous time steps to form a three-dimensional data matrix; extracting a spatial feature vector of each charging station; extracting a time feature vector of each charging station; generating a space-time fusion feature vector; constructing a multi-agent reinforcement learning prediction model, each charging station is defined as an agent, a global state vector is generated based on the local observation state of each agent and the global power grid state information, and the local value function of each agent is combined through a hybrid network to generate a global value function under the condition of meeting the monotonicity constraint to guide each agent to output a charging load prediction value at the next moment. The application deeply fuses the space-time features of the charging load, realizes multi-site collaborative prediction, and improves the prediction accuracy and robustness.

The application discloses an online fault diagnosis method for a hydro-generator based on a potential interval distance of a through screw, relates to the technical field of generators, and is suitable for detecting a stator winding ground fault. Voltage collection devices are additionally arranged on a plurality of through screws of the hydro-generator, real-time induced potential is acquired, a historical data matrix is established, and a data buffer matrix is updated at interval time; the data buffer matrix is divided into a data evaluation matrix, a fundamental wave and an odd harmonic phase are extracted through Fourier decomposition, a harmonic phase matrix is constructed, a phase difference matrix is obtained by calculating normalized phase differences of adjacent columns, a quartile distance is solved after being converted into a column vector array, a dynamic quartile distance array is formed, abnormal limit values are determined by combining a box plot principle and an error level of the voltage collection device, and faults are determined by traversing the array. The method does not need additional sensing devices and traditional threshold setting, is convenient to operate, has low implementation difficulty, and can accurately and timely identify faults.

The application provides a multi-station frequency agile radar anti-interference and synergistic method, through two short baseline pulse interval frequency agile radars receiving echo signals and carrying out mixing processing and pulse compression; interference pulses of an echo data matrix are removed and the remaining pulses are combined to obtain a new echo data matrix; a distance-speed two-dimensional redundant dictionary matrix corresponding to the new echo data matrix and matched with target information is constructed in combination with transmission parameters; then a compressed sensing model is constructed and a reconstruction result of an original signal is obtained by solving.The application selects two pulse interval frequency agile radars to work synergistically, which can effectively improve signal processing gain, has strong electronic countermeasure capability, good target detection capability and excellent electromagnetic compatibility; by utilizing the characteristic that only some pulses of echo of the pulse interval frequency agile radar may be interfered, the interfered pulses are directly removed in the echo data matrix, and complex matrix operation is not involved, so that the calculation complexity is low and the running speed is fast.

This invention discloses a page-turning method, system, electronic device, and storage medium based on the binary search method, improving page-turning efficiency and user experience in scenarios with a large number of pages. The method includes: page-turning preprocessing: including setting intermediate variables to identify the page search range and the current page; initializing the page-turning interval and button availability: determining the overall page range of the data matrix, establishing an initial search interval, and setting the initial availability of the "forward" and "backward" buttons according to the initial page situation; iteratively executing page-turning operations and updating the search interval: the user triggers intermediate page calculation by clicking the "forward" or "backward" button, updates the search interval according to the positional relationship between the target page and the current page, and repeats this process until the target page enters the page-turning display bar; when the page-turning termination condition is reached, the iteration stops, the target page is located, and the page-turning process is terminated; the page-turning result is output, and the status of the "forward" and "backward" buttons is adjusted according to whether the page-turning boundary or upper limit has been reached.

This application relates to a life cycle impact assessment calculation method suitable for GIS-LCA, including the following steps: S01) Obtaining the process spatial location set S p S02) Obtain a list D suitable for GIS-LCA; S03) Obtain the set of spatial locations of characteristic influencing factors S c S04) Construct the spatial data matrix C of the characteristic influence factors; S05) Construct the process location set P of the characteristic influence factors. s ;S06) will P s The parameterization process is transformed into a characteristic influence factor location matrix P; S07) Calculate the score matrix H of the characteristic influence factors in spatial location; S08) Based on the score matrix H, perform environmental assessment and interpretation of the target product. This application changes the existing LCIA calculation method that mechanically adopts the flow name correspondence method. This application can utilize geographic information system technology to achieve automatic flow correspondence by means of spatial relationships, so as to meet the GIS-LCA calculation requirements for LCIA.

The application discloses a kind of pure natural royal jelly freeze-dried powder detection methods, it is related to quality detection technical field, the method includes the following steps: collecting the multi-dimensional original data of royal jelly freeze-dried powder, data is handled to generate data matrix, from data matrix extraction key feature index set, to index set is analyzed and evaluated to generate authenticity score, generate digital detection report and quality spectrum, based on detection result is continuously optimized.The application, by fusing near-infrared spectrum, microscopic image, physicochemical activity and block chain traceability and other multi-source heterogeneous data, intelligent evaluation is carried out using knowledge graph and lightweight multi-modal network, realizes the multi-dimensional, high-precision analysis of the pure nature, naturalness and adulteration of royal jelly freeze-dried powder.

This invention discloses a method for training and optimizing a large-scale model in the thermal coal industry based on knowledge distillation, belonging to the field of large-scale model training and optimization technology. The method includes the following steps: collecting and preprocessing multimodal industry data to obtain a data matrix; inputting the data into a teacher's visual Transformer network to generate a sparse probability label matrix; constructing an improved TinyViT student network; embedding multiple optimization units in self-attention blocks; completing weight initialization; performing pre-training distillation to generate a composite distillation signal; inputting the signal into the student network for fine-tuning training to obtain a set of fine-tuned student weights; performing dynamic weighted training on hard samples; optimizing the training effect on long-tailed samples; and obtaining a lightweight model through quantized perceptual training and channel pruning. The model is then deployed to edge computing nodes for real-time inference. This invention improves the training efficiency of large-scale models in the thermal coal industry while optimizing the model's inference speed and accuracy, and can be effectively deployed in edge computing environments.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

This invention discloses a machine learning-based method and system for predicting groundwater level changes, relating to the field of hydrogeology. The method includes: standardizing multi-source time-series data to obtain a standardized multivariate time-series data matrix; constructing a supervised learning sample set; inputting the groundwater level sequence from the supervised learning sample set into a physically-guided variational mode decomposition network; decomposing the groundwater level sequence into K intrinsic mode component sequences and a residual term sequence using a loss function with physical-driven consistency constraints; for each of the K intrinsic mode component sequences, dynamically assembling a differentiable simulator from a library of differentiable simplified physical simulators, and co-training these simulators with the goal of approximating each intrinsic mode component sequence and reconstructing the original water level sequence as a whole, resulting in K fully trained assembled differentiable simulators. This invention generates reliable, visualized prediction results through multi-simulator collaborative extrapolation and uncertainty quantification.

The application discloses a kind of fault detection method and system for packaging equipment, it is related to industrial automation technical field;Method includes collecting the vibration waveform data of packaging equipment rotating shaft target sensor, vibration waveform data is preprocessed to obtain target data matrix;Final feature is obtained by substituting target data matrix into feature map extraction model;Feature extraction model is the model for extracting meaningful features in the target data matrix after preprocessing;Substitute final feature into node relationship model to obtain correlation matrix graph;Node relationship model is the model for converting spatial features in feature map into inter-node correlation matrix graph;Preset operation is carried out on correlation matrix graph to obtain fault cluster;Substitute fault cluster into database to compare data, obtain the root cause of fault, the efficiency of fault detection is significantly improved.

The application relates to the field of electric digital data processing and discloses a metal surface processing quality detection method and system, which comprises the following steps: acquiring a two-dimensional gray data matrix of a surface object to be detected; mapping the matrix into a frequency energy matrix by using a frequency transformation operator and determining an energy decision threshold; extracting a local energy peak value coordinate set and locking a retardation radius according to the radial energy distribution of the local energy peak value coordinate set, so as to generate a dynamic retardation mask matrix; logically point-multiplying the dynamic retardation mask matrix and the frequency energy matrix and rearranging a basic diffuse energy integral value, so as to generate a residual frequency domain matrix; and reconstructing a spatial domain data matrix by using an inverse transformation operator. The application realizes orthogonal decoupling of interference characteristics and target characteristics in the data dimension, relies on a mask generation logic driven by a data stream to cope with working condition fluctuations, and enhances the high-fidelity reconstruction strength for weak abnormal signals.

Various embodiments of the present disclosure provide a machine learning framework for machine learning classifiers based on labeled binary vectors for a data object. The techniques comprise generating a data matrix object based on a group of partially masked sets and a group of training predictions respectively generated by a pre-trained classifier using a group of partially masked sets, training a tabular machine learning model using the data matrix object as a training dataset, determining a set of importance scores that respectively correspond to the set of text segments based on one or more parameters of the tabular machine learning model determined during the training, and providing at least one text segment of the set of text segments to associate with the original prediction as a reason the original prediction was generated.

A kind of biological chip data feature engineering method based on statistical machine learning, comprising the following steps: generating data matrix;Carrying out z-score standardization;Value is calculated, and large-value gene data is screened;Correlation coefficient matrix is generated;Screening gene pair;Complex correlation coefficient is calculated;Mark gene complex correlation coefficient change.The present application is beneficial to analyze the correlation between data in a large number of biological chip data, and select a certain number of genes reflecting the difference between data groups according to the demand by using the method of feature selection.The present application uses correlation analysis statistics correlation coefficient, partial correlation coefficient and complex correlation coefficient for feature selection, which is beneficial to further reduce the data dimension, and is beneficial to predict the correlation change between two genes under different experimental treatment conditions.

The application discloses a mine hoist main shaft device fault detection method and system based on data driving, relates to the technical field of mine hoist data processing, and extracts a decentralized sequence of a vibration signal, scales output two-dimensional vector matrices at both ends by using a robust scale factor, solves an inter-end linear mapping matrix, calculates a residual error, generates a contact migration index, calculates a continuous wedge weight in combination with an index historical mean value, performs multiplication suppression on an inter-end consistency component, generates a stable input data matrix of stripped interference, calculates an end energy difference value, inputs the stable data into a convolutional neural network to generate a fault probability distribution, calculates and compares the priority of rope groove rope arrangement inspection and bearing main shaft maintenance by using bias intensity, wedge weight and fault probability, and outputs a final operation and maintenance instruction type. The application effectively decouples load mutation interference caused by a rope groove contact event, and avoids systematic misjudgment.

The application belongs to the field of tourism recommendation, and particularly relates to a similarity measurement method and system for complex landscapes, which comprises the following steps: obtaining multi-modal data time series of garden landscapes from a landscape training data set and converting the multi-modal data time series into time series vectors; training a feature extraction model according to a multi-modal time series data matrix to obtain an optimal feature extraction model; obtaining an optimal global vector by using the optimal feature extraction model and the multi-modal time series data matrix, and performing decoupling mapping to obtain an attribute vector; inputting the attribute vector and the training data set into a similarity model to optimize the similarity model and obtain an optimal similarity model; inputting actual landscape data into the optimal feature extraction model to obtain an actual global vector; and inputting the actual global vector into the optimal similarity model to obtain the similarity of different landscapes. The application can obtain more accurate similarity between gardens, provide reasonable basis for tourists' travel route planning, and thus better plan the travel route.

The invention discloses a power system fault detection method and device, equipment and a medium, and relates to the field of power fault detection, and the method comprises the steps: constructing a to-be-solved target state space based on the historical operation data of a target power system; performing low-rank decomposition on a data matrix constructed based on the historical operation data, solving the target state space to be solved according to an obtained decomposition result, and obtaining a target state space matrix of the target power system; determining a current system residual error based on a target Kalman filter constructed through the target state space matrix and the collected current input and output data, and splicing the current system residual error and a historical system residual error to obtain an augmented residual error vector; and calculating chi-square statistical magnitude based on the augmented residual vector, determining a target detection threshold according to a preset false alarm rate and the current degree of freedom, and if the chi-square statistical magnitude is continuously greater than the target detection threshold, performing power system fault alarm. Therefore, rapid and reliable power system fault detection can be realized.