142 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 present invention discloses a method for material classification and attribute extraction in the construction industry based on a large language model, including constructing a traditional database and a vector database based on a standard classification system data set and a full category data set; inputting irregular text, and retrieving the irregular text based on a large language model using a standard classification name vector database, a standard classification sample vector database, a full category classification name vector database, and a full category classification sample vector database to obtain retrieval information; performing preliminary matching and secondary matching on the retrieval information based on the large language model to obtain a material classification name; and extracting attributes of the material classification name from a traditional database based on the large language model to obtain material attributes. The present invention utilizes the semantic understanding ability of the large language model and the information retrieval mechanism to integrate multi-source data, thereby achieving efficient and accurate material classification and attribute extraction.

According to one embodiment, an anomaly factor estimation system includes a processor. The processor extracts a first acoustic feature amount based on a frequency, time, and signal intensity from sound data. The processor calculates a first reconstruction error that is a difference between the first acoustic feature amount and a reconstructed feature amount obtained by reconstructing the first acoustic feature amount based on a reconstruction model. The processor estimates an anomaly factor by performing vector search in a database with a query vector based on the first reconstruction error. The database stores a plurality of anomaly factors and of sample vectors based on a plurality of second reconstruction errors in association with each other.

The invention provides a bid invitation file qualification review processing method and system. The method comprises the following steps: constructing a qualification performance vocabulary; designing a query template to fit user input; constructing a sample vector data set; filtering user input based on the qualification performance vocabulary; similarity comparison is carried out by calculating the cosine similarity of keywords input by a user and the query templates in the sample vector data set, and the query template corresponding to the highest similarity is taken to obtain a recombined query statement; performing preliminary matching on the recombined query statement in a vector database; according to the existence of the text additional field in the screened result and the repetition rate of the text additional field content, screening to obtain a corresponding qualification performance term input by the user; and combining the corresponding qualification performance regulations input by the user with the keywords input by the user, and then inputting the combination into the LLM model to generate answers. According to the method, the accuracy of qualification legitimacy and compliance review of the bid inviting document can be 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 discloses a power transmission line risk monitoring method and device based on a learning rate strategy, electronic equipment and a storage medium, and belongs to the technical field of power transmission line risk monitoring. The method comprises the following steps: acquiring system operation information of a power system, wherein the system operation information comprises real-time meteorological data and line operation data; extracting real-time meteorological characteristics from the real-time meteorological data, extracting line operation characteristics from the line operation data, and constructing vector representation to be measured based on the real-time meteorological characteristics and the line operation characteristics; inputting the to-be-measured vector representation into a risk assessment model to obtain a risk assessment result output by the risk assessment model; the risk assessment model is obtained by training a logistic regression model based on a plurality of training samples and updating a self-adaptive learning rate, and the training samples comprise sample vector representations and risk assessment labels corresponding to the sample vector representations. The method can improve the assessment precision of the power transmission line risk, and guarantees the safe and stable operation of a power system.

The invention relates to the technical field of state prediction of hydroelectric power generation equipment, and discloses a cooler blockage prediction method based on deep learning coupling physical constraint, which comprises the following steps of: acquiring temperature, pressure difference and flow parameters during operation of a cooler, and generating time sequence data; preprocessing the time sequence data to generate a time sequence sample vector; constructing a deep learning hybrid model, extracting local features through the deep learning hybrid model, and capturing global time sequence dependence; combining a Navier-Stokes equation and a cross entropy to construct a comprehensive loss function; and inputting the processed data into the deep learning hybrid model, and outputting a cooler blockage state classification result. According to the method, early accurate recognition of the blockage state of the cooler can be achieved, the problem of response lag of a traditional method is solved, early warning can be given out in the early stage of blockage, the blockage type is recognized in advance, and non-planned shutdown is avoided.

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 discloses a power system probabilistic power flow method and system based on polynomial proxy and graph parallelism, and belongs to the technical field of power system planning and operation optimization. The method comprises the following steps: establishing a probability distribution model of a bus load prediction error to obtain sample distribution of predicted power; representing the output response quantity of the system as an input polynomial function, and establishing an initial polynomial agent model; determining an optimal configuration point set; mapping all the selected configuration points to an input sample space to obtain a small number of sample points of an input variable; performing deterministic load flow calculation on the sample points to obtain a sample vector of output response; calculating an undetermined coefficient in the proxy model according to the configuration point matrix and the sample vector; and solving the statistical characteristic quantity of the power flow response to obtain the probability distribution of the response. According to the calculation method provided by the invention, the probability distribution of the power flow response can be accurately and efficiently estimated, and convenience is provided for probabilistic power flow calculation of a novel power system under the access of a distributed power supply.

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 provides a method and equipment for identifying an Internet Control Message Protocol (ICMP) tunnel, and a computer program product. The method may comprise: obtaining ICMP traffic within a sampling unit time from an input signal; constructing an ICMP sample vector based on the ICMP traffic; providing the ICMP sample vector as input to a trained machine learning model; obtaining model output of the trained machine learning model; and identifying whether an ICMP tunnel exists in the ICMP traffic based on the model output.

A system and method to reduce fast Fourier transforms (FFT) required for bootstrapping in a Fully Homomorphic Encryption process. Ciphertext is separated into a vector of n samples. A fast Fourier transfer (FFT) is performed over a first vector of the samples. An FFT is performed for each of n polynomial terms multiplied by a bootstrap key. A point wise multiplication of each of the FFT outputs of the FFTs of the polynomial terms and the output of the FFT over the vector of the n samples is performed. The result of the FFT over the vector of the n samples is added to the results of the set of pointwise multiplications. An inverse FFT (IFFT) is performed on the FFT over the vector of n samples and the accumulated results of the point-wise multiplications to obtain a bootstrapping result of the ciphertext.

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.

A system and method to reduce fast Fourier transforms (FFT) required for bootstrapping in a Fully Homomorphic Encryption process. Ciphertext is separated into a vector of n samples. A fast Fourier transfer (FFT) is performed over a first vector of the samples. An FFT is performed for each of n polynomial terms multiplied by a bootstrap key. A point wise multiplication of each of the FFT outputs of the FFTs of the polynomial terms and the output of the FFT over the vector of the n samples is performed. The result of the FFT over the vector of the n samples is added to the results of the set of pointwise multiplications. An inverse FFT (IFFT) is performed on the FFT over the vector of n samples and the accumulated results of the point-wise multiplications to obtain a bootstrapping result of the ciphertext.

The invention discloses a speech imagination electroencephalogram signal recognition method based on multi-granularity information fusion, and the method comprises the steps: firstly collecting electroencephalogram data when a subject imagines multiple tasks, carrying out the preprocessing and feature extraction of the collected electroencephalogram data, and obtaining a sample vector and a label corresponding to the sample vector; secondly, a machine learning model combining view importance, sample importance and feature importance is constructed, and a joint optimization objective function is obtained; and then initializing weights of each view importance, sample importance and feature importance, and performing iterative optimization through an alternate optimization method according to a target function. And finally, after iterative optimization, inputting the sample vector into a machine learning model to obtain a prediction classification category of the speech imagination electroencephalogram signals. According to the method, information related to tasks can be effectively extracted from complex electroencephalogram signals, and it is ensured that a good classification effect can still be kept in a changeable environment.

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 invention discloses a query method based on retrieval enhancement generation, electronic equipment and equipment, and the method comprises the steps: obtaining a first question text, and correcting and recognizing the first question text to obtain a second question text; adding a mask to the second question text to obtain a retrieval question text; converting the retrieval problem text into a problem vector; calculating the similarity between the question vector and each question sample vector, and taking the original questions, answers and table structures corresponding to the first K question sample vectors with the highest similarity as retrieval results; on the basis of the second question text and the retrieval result, a cue word is constructed, the cue word is input into a pre-training generative model, and a structured query language is obtained; and obtaining a data query result according to the structured query language. According to the method, the context learning ability of the pre-training generative model is brought into full play by utilizing a retrieval enhancement generation technology, the accuracy of structured query language generation is improved, and the accuracy of a data query result is improved.

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 present invention relates to a probabilistic power flow method and system for power systems based on polynomial surrogate and graph parallelism, belonging to the technical field of power system planning and operation optimization. It includes establishing a probability distribution model of the bus load prediction error to obtain the sample distribution of the predicted power; representing the output response quantity of the system as a polynomial function of the input and establishing an initial polynomial surrogate model; determining the optimal set of collocation points; mapping all selected collocation points to the input sample space to obtain a small number of sample points of the input variables; performing deterministic power flow calculations on the sample points to obtain the sample vector of the output response; calculating the undetermined coefficients in the surrogate model according to the collocation point matrix and the sample vector; and solving the statistical characteristic quantities of the power flow response to obtain the probability distribution of the response. The calculation method provided by the present invention can accurately and efficiently estimate the probability distribution of the power flow response, facilitating the probabilistic power flow calculation of a new power system under the access of distributed power sources.

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 a bridge support displacement early warning method, which comprises the following steps of: firstly, collecting early-stage bridge temperature and support displacement data, preprocessing the data, and removing abnormal values to serve as a training set; then establishing a temperature-displacement prediction model; combining the temperature sample vector and the displacement data corresponding to the time sequence, calculating the mean value and the covariance of the to-be-measured sample vector, calculating the posterior probability of the displacement data corresponding to the temperature sample to which the to-be-measured sample vector belongs, obtaining a displacement predicted value according to Gaussian distribution characteristics, and obtaining the difference between a displacement measured value and the predicted value; and then defining an early warning index by using a mahalanobis distance to obtain an early warning threshold value, and finally performing standardization processing on an upper calculation value as an early warning value. According to the invention, the service performance of the bridge support can be observed online in real time, the cost is low, the safety performance of a bridge structure can be effectively improved, the interference of a temperature factor on a monitoring result can be effectively eliminated, the early warning accuracy of displacement is extremely high, and the false alarm rate is low.

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.