107 results about "Sample vector" patented technology

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention discloses a cross-language code semantic alignment method, which constructs a shared semantic space through a unified abstract syntax tree (AST) and a graph matching network (GMN) so as to reduce the difference of different programming languages in syntax structure and node representation. The method comprises the following steps: (1) mapping a multi-language AST node to a unified general label set and performing structure enhancement; (2) performing node feature coding on the unified AST, and realizing cross-language interaction in combination with a cross-graph attention mechanism; (3) node representation is generated through intra-graph loop updating, and an overall semantic vector is obtained through global attention pooling; and (4) through comparative learning training in the shared space, the distance between semantically equivalent positive sample vectors is shortened, and the distance between non-equivalent negative sample vectors is shortened, so that the discrimination capability of cross-language semantic representation is enhanced. According to the method, the semantic consistency of the functional level can be effectively captured, and the accuracy and efficiency of cross-language code understanding, multiplexing and retrieval are remarkably improved.

The invention discloses a document proofreading system and method based on artificial intelligence, and relates to the technical field of natural language processing, and the method comprises the steps: analyzing a document, dividing semantic units, and obtaining a document with a context label based on context type library labeling; performing initial error detection by using the corresponding rule set, and comparing the format of the document and the text quality characteristics with a standard sample vector space to obtain an initial error set; constructing a defect conduction chain network, and locating root cause error nodes causing a plurality of secondary errors by tracing the defect conduction chain network; generating an intelligent proofreading report; receiving a new-version document revised by the user, comparing the new-version document with the original-version document, positioning a change area, and analyzing a quality difference of the change area on a related quality dimension; based on the quality difference, an incremental proofreading report is output, and the incremental proofreading report comprises newly introduced errors and unsolved root cause errors.

The invention discloses an electric energy quality disturbance positioning identification method based on phase perception and multi-mode fusion, and relates to the technical field of electric power system monitoring and fault diagnosis. The electric energy quality disturbance positioning identification method based on phase perception and multi-modal fusion comprises the following steps: acquiring an original voltage signal of a three-phase electric power system; extracting a sample embedding vector of each phase based on the original voltage signal, comparing the sample embedding vector with a normal sample vector, and determining a disturbance phase with power quality disturbance in combination with an adaptive threshold value; performing time sequence feature vector extraction and bispectrum image feature extraction on the disturbance phase, and performing alignment and collaborative fusion to obtain fusion features; the electric energy quality disturbance type is identified based on fusion features, phase-level positioning of the three-phase PQD is achieved, through an innovative phase perception embedding and prototype comparison mechanism, accurate and rapid positioning of abnormal phases in a three-phase system is achieved for the first time under the condition of not depending on disturbance phase labels, and the core pain point of a traditional method is solved.

The invention relates to the technical field of power systems, in particular to a power grid fundamental harmonic and inter-harmonic detection method and device and a storage medium, and can collect power grid signals, arrange discrete samples into sample vectors, apply a Hanning window to the sample vectors for zero filling DFT, and obtain a frequency index of fundamental waves according to a peak value of frequency spectrum amplitude square; setting a harmonic wave search range according to the frequency index of the fundamental wave, and screening the harmonic waves according to the frequency related threshold to obtain a frequency index set of the fundamental wave and the harmonic waves; calculating a residual error between the power grid signal and the fundamental harmonic reconstruction signal, and calculating a residual error ratio; and performing secondary iteration on the residual error to obtain a TF coefficient corresponding to the inter-harmonic. According to the technical scheme, fundamental wave phasor measurement, harmonic detection and inter-harmonic detection can be completed at the same time, the precision is high, and the detection efficiency is high.

The invention provides a process detection method and device, equipment and a medium. Comprising the following steps: determining a target detection model, and determining sample vectors corresponding to a plurality of sample images and a similarity threshold according to the target detection model; according to the target detection model, detecting the to-be-detected image frame to obtain a corresponding target image, and performing feature extraction on the target image to obtain a target vector; calculating similarities between the target vector and the plurality of sample vectors, and comparing each similarity with a similarity threshold to obtain a comparison result; determining a process detection result according to the comparison result, and sending a result verification request to the user according to the process detection result; receiving feedback information corresponding to the result verification request, and adjusting the similarity threshold according to the feedback information, the target vector and the plurality of sample vectors to obtain an adjusted similarity threshold; therefore, the flexibility and accuracy of process detection are improved.

The invention discloses a real-time image feature matching method based on spatial distribution consistency. The method comprises the following steps: acquiring a high-resolution to-be-matched image with an overlapping region; performing SIFT feature point detection on the image, and extracting descriptor vectors corresponding to SIFT feature points; calculating the Euclidean distance between the descriptors corresponding to the SIFT feature points; matching the descriptors corresponding to the SIFT feature points to obtain initial matching point pairs; two-dimensional sample points are constructed for clustering, correct matching point pairs and mismatching point pairs are separated, and coarse screening of the matching point pairs is achieved; constructing a four-dimensional sample vector based on the residual matching point pairs after coarse screening; and clustering the constructed four-dimensional sample vectors, separating correct matching point pairs from mismatching point pairs, and realizing fine screening of the matching point pairs. The method can adapt to complex application scenes such as large view angle change, non-rigid deformation and non-parametric geometric constraint, and the matching accuracy, stability and matching efficiency and precision are remarkably improved.

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses an IC carrier plate detection method and system based on multi-mode laser imaging and AI fusion. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The application discloses a malicious traffic detection method, system and device and a storage medium, and belongs to the technical field of network traffic analysis and cyberspace security application, and the method comprises the following steps: obtaining traffic statistical information to be detected, and performing format preprocessing on the traffic statistical information to obtain a sample vector; inputting the sample vector into a pre-trained neural network partial framework search network model to obtain a prediction vector; the prediction vector comprises a plurality of prediction values, each prediction value comprises a classification label of itself, a classification label of a maximum prediction value is selected as a final classification label, if the final classification label is malicious, then traffic corresponding to the traffic statistical information is malicious traffic, otherwise, the traffic is non-malicious traffic; the category of the traffic can be determined without manual feature design; by using a relatively light model, the calculation amount is reduced, the model can be deployed on an edge computing node, the feature extraction capability and practicability are enhanced, and the problems of insufficient precision and insufficient universality are overcome.

The invention belongs to the technical field of ocean exploration, and particularly relates to an ocean exploration wave field reconstruction method based on dictionary learning, and the method comprises the steps: carrying out the preprocessing of original wave field data, and obtaining the preprocessed wave field data; separating the preprocessed wave field data into non-overlapping block sample matrixes; converting all block sample matrixes into a sample vector set; selecting a training sample set from the sample vector set; adopting the training sample set to construct a double-sparse dictionary model; iteratively updating the double sparse dictionary model by adopting an alternate optimization mode to obtain a final sparse dictionary and a sparse coefficient matrix corresponding to the sparse dictionary; and carrying out sparse reconstruction on the actual observation wave field by adopting the final sparse dictionary and the sparse coefficient matrix corresponding to the final sparse dictionary. The method has high adaptability to noisy or under-sampled wave field data, and a continuous and high-fidelity wave field structure can still be recovered even under the condition that data acquisition conditions are limited.

The disclosure provides a method of detecting arc faults in a DC power distribution system. The method comprises: sampling, using one or more sensors, a voltage signal on a conductor forming a current path of the power distribution system to generate a voltage signal sample vector; sampling, using the one or more sensors, a current signal in the conductor to generate a current signal sample vector; generating one or more feature vectors based on the voltage signal sample vector and the current signal sample vector and having fewer elements than the current signal sample vector or the voltage signal sample vector; providing the one or more feature vectors to an arc fault classification model; and receiving as an output of the arc fault classification model an indication of whether an arc fault is occurring in the power distribution system.

The application discloses an anchor point guided clustering based data classification method, device and equipment, relates to the technical field of digital data processing, and comprises the following steps: obtaining an original data set to be classified, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining the anchor point matrix and the clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree is taken as the final category label of the sample vector, and the classification results of all sample vectors are output. The application solves the problems that the existing fuzzy clustering method is sensitive to initial conditions, is easy to fall into a suboptimal solution, leads to unstable and inaccurate classification results, and cannot be directly solved by using a gradient descent algorithm, and is difficult to be applied to large-scale data sets, and realizes the enhancement of complex data classification precision and the applicability in different data scale scenes.

Provided are a data retrieval method and device, a data processing method and device, equipment and a medium, which are related to the technical field of computers and can be used for data retrieval and data clustering. The implementation method comprises the following steps: in response to receiving a to-be-retrieved vector, determining at least one to-be-retrieved storage medium corresponding to the to-be-retrieved vector; for each to-be-retrieved storage medium in the at least one to-be-retrieved storage medium, using a storage controller corresponding to the to-be-retrieved storage medium to extract a first number of sample vectors from at least one sample vector stored in the to-be-retrieved storage medium, the similarity of the first number of sample vectors to the to-be-retrieved vector being higher than the similarity of other sample vectors in the at least one sample vector to the to-be-retrieved vector; and determining a retrieval result corresponding to the to-be-retrieved vector based on the first number of sample vectors from each of the at least one to-be-retrieved storage medium.

This application discloses a training method for a source evaluation model, a source evaluation method, and related products. Text sample sequences and frequency sample sequences are input into the evaluation model to be trained. The model encodes the text sample sequences and frequency sample sequences to obtain text sample vectors corresponding to the text sample sequences and frequency sample vectors corresponding to the frequency sample sequences. The evaluation model then performs prediction and evaluation processing on the text sample vectors and frequency sample vectors to obtain source evaluation prediction results. Based on the difference between the source evaluation result labels and the source evaluation prediction results, the parameters of the evaluation model are adjusted until the adjusted model meets the model training cutoff condition, and training ends to obtain the source evaluation model. Thus, this application can construct a source evaluation model based on the source sample name and the title, keywords, and publication frequency of the published sample text as features, thereby achieving the evaluation of the source.

The invention provides an anti-coherent interference target detection method and system based on classification assistance, and the method comprises the steps: carrying out the preprocessing of an echo received by a linear array in a detection region, and obtaining a to-be-detected sample vector and an auxiliary sample vector; independently and identically distributed discrete random variables are introduced to obtain a probability density function of a to-be-detected sample vector; calculating the posterior probability of the classification of the to-be-detected sample vector by using the E step of the EM algorithm, and obtaining the interference covariance matrix, the target echo, the signal amplitude of the CJ, the posterior probability of the classification of the to-be-detected sample vector and the estimated value of the incident angle in the M step according to the auxiliary sample data set; and obtaining a self-adaptive detector based on LRT according to the obtained estimation value, and realizing anti-coherent interference target detection. The method has the advantages that unit samples containing different signal components can be automatically recognized and processed, and the efficiency and accuracy of interference / target detection are improved.

The invention belongs to the field of magnetic sensors, and provides a nested neural network-based array current sensor rapid resolving method, which comprises the following steps of: S1, acquiring magnetic field detection sample vectors of a magnetic sensor array under different working conditions, and taking a lead standard current value as label data of the sample vectors, constructing a data set of a first neural network; s2, dividing the data set in the S1; s3, constructing a first neural network model by using the data set divided in the S2; s4, in an actual measurement environment, inputting an output signal of the magnetic sensor array on the measured wire into the first-layer neural network model to obtain a current of the measured wire; forming a second neural network data set, and dividing the second neural network data set; s5, constructing a second neural network model by using the data set divided in the step S4; and S6, obtaining a predicted current value of the current to be measured by using the second neural network model. According to the invention, high-precision rapid calculation of the current in a complex interference environment can be realized.

The invention relates to a permanent magnet synchronous motor model prediction control method and system, belongs to the technical field of permanent magnet synchronous motor driving, and solves the problem that parameter robustness and dynamic performance cannot be considered when parameter mismatch occurs in permanent magnet synchronous motor model prediction control in the prior art. Comprising the steps of obtaining sampling values of stator current, stator voltage and electrical angular velocity at a current sampling moment and two previous moments and a direct current bus voltage at the current sampling moment, further updating incremental current prediction errors and time sequence sampling data of incremental voltage, and further obtaining a first sample vector and a second sample vector at the current sampling moment; and then a small-batch stochastic gradient descent method is adopted to obtain an identification value of a parameter mismatch coefficient at the current sampling moment, then stator current prediction values of different voltage vectors at the next moment are obtained, then an optimal voltage vector is obtained, and permanent magnet synchronous motor model prediction control at the next moment is carried out based on the optimal voltage vector.

The application discloses a SNP genotype coding method, comprising the following steps: obtaining genomic selection data and a genomic annotation file, wherein the data comprises SNP input data; the SNP input data comprises chromosome number and SNP position; sorting columns of a data table according to the chromosome number and the SNP position; preprocessing the data table to obtain SNP coding; extracting the genomic annotation file to obtain SNP type information; forming SNP columns in the data table at the SNP positions, traversing adjacent SNP columns, storing the SNP columns into channels, and completing the channels; and coding the filled SNP based on the SNP type information and the SNP coding to obtain a sample vector. The sample vector can be used as an input of a convolutional neural network, so that information contained in the genomic annotation file can be applied to deep learning.

System, comprising: a fleet of vehicles, each vehicle (10) comprising: a climate controller (12) with a manual temperature control mode and an automatic temperature control mode, wherein the automatic temperature control mode is configured to control climate actuators (18) in the vehicle (10) in response to a model (13) that links detected climate conditions in the vehicle (10) with respective operating settings; a buffer memory (20) configured to periodically store sampling vectors consisting of respective operational settings and respective detected climate conditions; a user interface in the vehicle (10) that is configured to respond to a user's cancel commands to change respective operational settings while the automatic temperature control mode is running; and a wireless communication system configured to send data packets to a remote server (24) when the user generates a cancel command, each data packet consisting of multiple stored sample vectors and a label for the cancel command; and a central database associated with the remote server (24) which is set up to receive the data packets from the vehicle fleet in order to identify patterns within the received sample vectors that are associated with the same cancel command.

An apparatus and method for determining a frequency reference offset between a transmitter and a receiver. Time sample vectors are created, where each time sample vector has a sequence of time samples of a received chirp period within a sequence of chirp periods. For each pair of time sample vectors in the time sample vectors, a product vector of each pair of time sample vectors is computed. Each product vector of each pair of time sample vectors is added to result in an accumulated value vector. A discrete Fourier transform of the accumulated value vector is computed. A detected frequency is determined based on a frequency corresponding to an element of the discrete Fourier transform that has a higher magnitude than other elements of the discrete Fourier transform. A frequency reference offset between a transmitter of the received chirp period and a receiver of the received chirp period is determined based on the detected frequency.

The invention discloses a second-hand car price evaluation model training method and device based on deep learning, and the method comprises the steps: obtaining the historical transaction data of a second-hand car, and carrying out the data preprocessing, and obtaining a training sample; selecting samples from the training samples to construct a sample pair of the to-be-evaluated sample and the real sample; respectively inputting the real sample and the to-be-evaluated sample into a first sub-network and a second sub-network of the double-tower model to obtain a first sample vector and a second sample vector; wherein the first sub-network and the second sub-network both adopt a deep cross network architecture; performing element-level product on the first sample vector and the second sample vector to obtain an interactive feature vector, and mapping the interactive feature vector into a scalar through an output layer; all parameters of the double-tower model are updated through back propagation according to the scalar and the loss function of the sample price difference, a trained second-hand car price evaluation model is obtained, and the problems that a second-hand car price prediction method is weak in feature intersection and insufficient in sample relevance utilization are solved.

The present invention discloses a small sample classification method and device based on a random projection metric space, relating to the field of natural language processing technology. The method comprises: obtaining multiple tasks to be classified; inputting the multiple tasks into a classification model based on the random projection metric space; and obtaining classification results for the multiple tasks based on the multiple tasks and the classification model based on the random projection metric space. The present invention constructs a random metric space adapted to specific tasks, constructs a metric space based on task features by fine-tuning the position of sample vectors in the metric space, and specifically uses random vectors to learn the metric space for each task, thereby resolving the problem that the general practice of using a universal metric space is not well suited for all tasks and lacks applicability.

The application discloses a metabolomics sample normalization method and system based on local similarity, and the method comprises the following steps: an initialization step, giving a metabolomics data matrix to be normalized and initializing a normalization coefficient of the sample; a projection step, projecting the data matrix into a principal component analysis subspace to obtain a projected data matrix; a nearest neighbor set construction step, calculating the correlation between any two samples by using the data matrix, calculating the distance between any two samples by using the projected data matrix, and obtaining the nearest neighbor set of each sample according to the correlation and distance indexes; a sample normalization step, calculating the normalization coefficient of the sample vector to obtain a normalized new data matrix; and an iteration step, iterating the subspace projection step to the sample normalization step until the relative change amount of the normalized data matrix is less than a given threshold. The application retains the local structure of the data, corrects the dilution effect, and makes the sample data more comparable.

The application relates to a computing power cloud large language model training optimization method, which comprises the following steps of: in an offline tool software, Tokenizer internal order is disturbed to obtain a new Token sequence; an Embedding matrix is adjusted according to corresponding positions according to the new Token sequence to obtain a new Embedding matrix corresponding to the new Token sequence; the Tokenizer is retrained according to the new Token sequence and the new Embedding matrix; a corresponding Tokenized sample vector is generated according to the retrained Tokenizer; a large language model after modification is tested, sample vectors and corresponding Embedding matrices are uploaded, and a supervised fine-tuning process is performed. The application can reduce the risk of data leakage, and it is beneficial for computing power service providers and users to reduce the risk of data leakage. The application can reduce the possibility of data leakage for service providers and make users more confident in using cloud services. No additional calculation and storage are introduced. Generally, users renting computing power cloud need to use GPU or TPU acceleration functions, and the Tokenize process generally uses the calculation capacity of CPU, so that Tokenize on the user side does not introduce additional calculation and storage.

The invention relates to the field of data processing, in particular to a heart age determination method and device, electronic equipment and a storage medium, and the method comprises the steps: minimizing the distance between heart health characterization vectors of the same heart health state output by an initial heart health encoder; performing updating iteration on the initial heart health encoder to obtain a first determination model by taking maximization of the distance between the heart health characterization vectors of different heart health states as a target; updating and iterating the first heart health encoder according to the heart fusion feature sample vector and the heart activity state and the relative heart age thereof to obtain a second determination model; and taking the heart fusion feature sample vector as a sample and the heart biological age as a label to update and iterate an age prediction module to obtain a target determination model so as to determine the heart age. According to the method and the device, the heart age can be accurately and efficiently determined through the trained determination model.

The present invention discloses a three-dimensional biomimetic design method based on a generative adversarial network. The method comprises the following steps: Step 1: Establishing a training set; Step 2: Establishing a deep generative model and training the deep generative model using the training set; the deep generative model comprises an implicit autoencoder (3DCNN) and a latent vector generative model; the implicit autoencoder comprises an encoder (3DCNN) and a decoder (IM-Decoder); the latent vector generative model employs a generative adversarial network; and Step 3: Using the deep generative model to generate a three-dimensional biomimetic object. The present invention also discloses a system for implementing the above method, comprising a data preprocessing module, a deep generative model, a sample vector repository, and a post-processing module.

The present invention relates to the technical field of hydropower equipment state prediction. The present invention discloses a cooler blockage prediction method based on deep learning coupled with physical constraints, comprising the following steps: collecting the temperature, pressure difference, and flow parameters of the cooler during operation to generate time series data; preprocessing the time series data to generate time series sample vectors; constructing a deep learning hybrid model to extract local features and capture global time series dependencies through the deep learning hybrid model; combining the Navier-Stokes equations and cross entropy to construct a comprehensive loss function; inputting the processed data into the deep learning hybrid model to output a cooler blockage state classification result. The present invention can achieve early and accurate identification of the cooler blockage state, solving the problem of delayed response of traditional methods. It can issue an early warning of blockage, identify the blockage type in advance, and avoid unplanned downtime.

This invention discloses a training method for an equipment fault diagnosis model and a method for equipment fault diagnosis, relating to the fields of fault diagnosis and deep learning technologies. The method includes: acquiring at least one sound sample data and corresponding label data; processing the sound sample data through a background noise suppression channel and an impulse interference suppression channel to obtain a denoised sound sample vector; performing dual-tree complex wavelet packet decomposition on the denoised sound sample vector to obtain a sample time-frequency feature matrix; processing the denoised sound sample vector to obtain a sample operating condition auxiliary feature vector; the sample operating condition auxiliary feature vector is used to characterize the equipment operating condition state corresponding to the sound sample data; and training a deep learning model based on the sample time-frequency feature matrix, the sample operating condition auxiliary feature vector, and the label data to obtain an equipment fault diagnosis model. The above technical solution can improve the accuracy of equipment fault diagnosis.

The invention discloses a data classification method, device and equipment based on anchor guide clustering, and relates to the technical field of electrical digital data processing, the method comprises the following steps: obtaining a to-be-classified original data set, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining an anchor point matrix and a clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree value serves as a final category label of the sample vectors, and classification results of all the sample vectors are output. The problems that an existing fuzzy clustering method is sensitive to initial conditions and is prone to falling into a suboptimal solution, so that a classification result is unstable and inaccurate, and a gradient descent algorithm cannot be directly used for solving and is difficult to adapt to a large-scale data set are solved; the method achieves the enhancement of the classification precision of complex data and the applicability in different data scale scenes.

The application discloses a kind of magnetic sensor array anti-interference current measurement method and system based on neural network, the method of the application includes respectively collecting the magnetic field detection value sample vector of magnetic sensor array under different wire current, wire eccentricity, crosstalk current interference, stray magnetic field interference and external temperature and humidity variation;Wire current is used as label data to construct training data set;According to the preset proportion, training set and test set are divided;Machine learning model is constructed, and machine learning model is trained using training set and test set in combination with preset loss function and optimization algorithm;The magnetic field detection value sample vector detected by the magnetic sensor array on the measured wire is input into the trained machine learning model to obtain the wire current of the measured wire.The application aims at the problem that current sensor is easily subjected to external current magnetic field interference, resulting in the decline of current measurement accuracy, and provides a kind of current measurement technical scheme for effectively resisting external magnetic field interference using artificial neural network.