151 results about "Result vector" patented technology

The invention discloses a low sample hydro-generator fault diagnosis method based on transfer learning. The method comprises the following steps: collecting a current signal, a vibration signal, a temperature signal and a voiceprint signal; executing preprocessing; performing label labeling, and dividing the data into a source domain data set and a target domain data set; operating parameters of the hydro-generator are collected, processed and combined into working condition feature vectors; training a source domain base model on the source domain data set to generate a pre-training parameter set; establishing a physical constraint module, and binding the physical constraint module with the parameter updating process of the source domain base model; initializing a transfer learning model, and calling a physical constraint module to apply constraint to generate a physically constrained transfer learning model; performing increment fine adjustment; and outputting a diagnosis result vector through the transfer learning model after increment fine tuning. According to the method, transfer learning and physical constraints are combined, hydro-generator fault diagnosis is optimized through increment fine tuning, and the method has the advantages of high precision, small sample adaptability and physical consistency verification.

The invention discloses an analysis method for applying a dense model to big data, and the method comprises the following steps: S1, collecting and preprocessing heterogeneous original data, and generating a standardized data set; s2, performing feature extraction and feature splicing on different types of fields in the standardized data set to form a preliminary feature vector matrix; s3, inputting the initial feature vector matrix into a heterogeneous feature fusion and completion algorithm to generate a fused feature vector matrix; s4, inputting the fusion feature vector matrix into a deep dense residual network model, and outputting deep semantic feature representation; s5, inputting the deep semantic feature representation into a full-connection output layer to generate an analysis result vector; s6, constructing a loss function, and executing back propagation to optimize weight parameters in the deep dense residual network model; and S7, repeatedly executing the steps S1 to S6 on the newly added data. According to the method, deep residual network modeling and a swarm intelligence anomaly recognition mechanism are fused, and unified modeling, deletion completion and high-dimensional feature expression analysis of big data multi-source fields are realized.

The present application discloses a data processing method and apparatus, a device, and a readable storage medium. The method comprises: combining M vision mapping vectors generated from media data and N text mapping vectors generated from text information into a mapping vector sequence, and in the mapping vector sequence, inserting a compression token vector between the M vision mapping vectors and the N text mapping vectors to obtain a vision compression sequence; performing attention processing on the vision compression sequence to obtain an attention result vector; determining a unit attention vector associated with the compression token vector in the attention result vector as a global compression vector, the vector length of the global compression vector being less than the sum of vector lengths of the M vision mapping vectors; and generating a question-answer result on the basis of the global compression vector and unit attention vectors associated with the N text mapping vectors. By using the present application, vision mapping vectors can be compressed, thereby reducing calculation costs while improving model performance.

The embodiment disclosed by the application belongs to the technical field of computing, and particularly relates to a method and device for performing stencil calculation and a computing device. The method comprises the following steps: acquiring a plurality of node data included in a calculation region, and grouping the plurality of node data into a data vector. According to at least one calculation condition included in the stencil calculation, the plurality of node data are subjected to judgment processing, and a judgment result corresponding to each calculation condition is obtained for each node data. For each calculation condition, the judgment result corresponding to the calculation condition is grouped into a judgment result vector, and the judgment result vector is obtained. Based on the data vector and the judgment result vector corresponding to each calculation condition, a calculation operation corresponding to each calculation condition in the stencil calculation is performed, and a calculation result of the stencil calculation is obtained. The application can convert the stencil calculation into vector calculation, and can improve the efficiency of the stencil calculation.

The invention provides a retrieval enhancement generation method and a retrieval enhancement generation system. The retrieval enhancement generation method comprises the following steps: segmenting basic knowledge text segments and carrying out semantic vectorization processing; establishing a historical relation graph; carrying out vectorization processing on nodes of the historical relation graph; obtaining a direct query result vector and a corresponding direct matching degree according to user query content; obtaining similar historical query vectors and corresponding similar historical query result vectors according to user query contents; calculating a historical matching degree through the direct query result vector and the similar historical query result vector; and calculating the overall matching degree, reordering the direct query result vectors, and then generating cue words and inputting the cue words into the large model. According to the technical scheme, the similarity analysis can be performed on the historical query result confirmed by the user and the direct query result, and the direct query result is reordered, so that the target query result which is more accurate and better meets the user requirement is obtained.

The invention discloses a multi-modal retrieval method and device, and relates to the technical field of multi-modal retrieval, and the method comprises the steps: obtaining a query vector corresponding to a user query text, and carrying out the query in a vector database according to the query vector, and generating a query result; vector codes corresponding to the multi-level content block structures corresponding to the multiple multi-modal table documents are stored in the vector database; the multi-level content block structure of any multi-modal table document comprises an atomic layer, a semantic layer and a relation layer; the atomic block comprises target element content and structure information of any cell in the multi-modal table document; the semantic block comprises aggregation content corresponding to any row, any column or any whole table in the multi-modal table document; the relation block comprises a text for describing the incidence relation between the multiple atomic blocks and the incidence relation between the multiple semantic blocks; the structure information comprises row and column indexes corresponding to each cell and a cross-modal association relationship among the multi-modal elements. According to the method, the multi-modal retrieval accuracy can be improved.

The embodiment of the invention provides a method and device for calculating a matrix multiplied by a vector, computing equipment and a storage medium, the matrix is a matrix of M * K, the vector is a vector of K * 1, and M and K are positive integers. The method comprises the following steps: converting an M * K matrix into a first tensor of M * (K / N) * N, N being a positive integer, Ngt; 1 and K are multiples of N; converting the vector of K * 1 into a second tensor of N * (K / N); using a tensor calculation kernel to carry out general matrix multiplication calculation of a first tensor of M * (K / N) * N and a second tensor of N * (K / N) in batches to obtain M result matrices; elements on diagonals of each of the M result matrices are added to result in a matrix of M * K multiplied by each element on a result vector of M * 1 of the vector of K * 1. According to the scheme, high-throughput and low-delay high-speed matrix vector multiplication (MMV) operation is realized by utilizing the tensor calculation kernel.

The invention belongs to the technical field of information security, and discloses a privacy protection decision tree evaluation method based on packaged homomorphic encryption, which comprises the following steps: acquiring a cryptographic algorithm, a public key and a key # imgabs0 #, and processing and encrypting an original feature vector; mixing the decision tree model, extracting a model structure index # imgabs1 # and a coded model structure, and extracting a feature value of a ciphertext from an encrypted feature vector based on the model structure and a cryptographic algorithm; on the basis of the model structure and a cryptographic algorithm, comparing a characteristic value with a threshold value of a node of the decision tree model under a ciphertext to obtain an encrypted comparison vector # imgabs2; generating a path evaluation result # imgabs5 # based on # imgabs3 #, the model structure and # imgabs4 #; mixing the # imgabs6 # to generate an encrypted evaluation result vector # imgabs7 # and related mixing information; and based on # imgabs8 #, # imgabs9 # and related confusion information, obtaining an evaluation value of the original feature vector. The method gives consideration to privacy protection and calculation efficiency, and is high in model applicability.

Aspects for forward propagation of a convolutional artificial neural network are described herein. The aspects may include a direct memory access unit configured to receive input data from a storage device and a master computation module configured to select one or more portions of the input data based on a predetermined convolution window. Further, the aspects may include one or more slave computation modules respectively configured to convolute a convolution kernel with one of the one or more portions of the input data to generate a slave output value. Further still, the aspects may include an interconnection unit configured to combine the one or more slave output values into one or more intermediate result vectors, wherein the master computation module is further configured to merge the one or more intermediate result vectors into a merged intermediate vector.

Vector dataset index parameter determination is performed by building an index of a vector dataset according to each build parameter group among a plurality of build parameter groups, generating search parameter groups, submitting each vector query in a vector query set to each index according to the candidate search parameter values from each search parameter group to obtain, for each submission, a result content and a result delay value, obtaining, for each submission, a vector distance between at least one result vector of the result content and the submitted vector query, determining a relative accuracy value for each submission by comparing the vector distance of the result content to the vector distance of at least one other submission, correlating build parameter values and search parameter values with the relative accuracy values and the result delay values, and fixing build parameter values based on the correlating.

Aspects of the disclosure are directed to an architecture including a dynamic serialization buffer and / or dynamic deserialization buffer coupled between a vector processing unit and a matrix multiplication unit. The dynamic serialization buffer and / or dynamic deserialization buffer allow for streaming any integer of vectors per cycle when performing acceleration of matrix multiplication operations. The matrix multiplication unit receives vectors equivalent to an amount of data from the vector processing unit at an arbitrary rate of vectors per cycle. The matrix multiplication unit processes the vectors to generate resulting vectors that are output at the arbitrary rate.

The application discloses an edge computing platform and system based on FPGA real-time target recognition detection, and relates to the field of microelectronic chips.The edge computing platform comprises an interconnected FPGA and DDR, and an ISP module, a pre-processing module, a VDMA module, an inference accelerator, a CPU and a character superposition module are arranged on the FPGA.The inference accelerator is used for deploying a preset convolutional neural network model, reading any frame of second image and weight parameter data about convolution kernels in the convolutional neural network model from the DDR, accelerating the execution of the algorithm of the convolutional neural network model by using a sphygmic array cluster module, and generating an inference result vector output to the DDR;the sphygmic array cluster module is integrated with a Winograd fast convolution algorithm and a multi-channel sphygmic array.Compared with the prior art, the application realizes the accelerated operation of the convolutional neural network model, and thus improves the inference efficiency.

The invention relates to the technical field of artificial intelligence and data processing, and discloses an intelligent information decision-making system based on reinforcement learning, and the system comprises an information perception module which is used for extracting concept nodes from an external information flow; the causal learning and evolution module is used for executing actions through a strategy network to update the dynamic causal graph and outputting a decision starting signal; the reward and strategy updating module is used for generating a mixed reward after the graph is updated and updating the strategy network; the candidate decision generation module is used for generating candidate actions by utilizing a strategy network and a causal graph after receiving the decision starting signal; the anti-fact deduction module is used for deducing the candidate actions by utilizing a causal graph to generate an expected result vector; and the decision refining module is used for integrating the candidate action and the expected result and outputting a final decision result. According to the method, through dynamic causal modeling, the problems of model stiffness and opaque decision are solved, and scientific and adaptive reliable decision is realized.

The invention relates to the field of cross application of electronic data processing and block chain technologies, in particular to a tax declaration data verification method based on a block chain. Comprising the following steps: outputting a global data abstract through an SHA-512 hash function; performing Hash processing to obtain field-level structural features, and generating structural perturbation terms in combination with the global data abstract; carrying out nonlinear transformation and weight weighted fusion, and packaging into a ciphertext; analyzing the tax declaration data ciphertext and the global abstract structure, and extracting a structural feature vector; matching the rule template library on the chain to generate a verification function set, outputting a Boolean type result vector, and generating a cross-chain key identifier; a comprehensive fusion credible score value is generated through nonlinear fusion, and whether the tax declaration data meet verification requirements in the aspects of structure verification and cross-chain consistency or not is judged. The technical problems that in the prior art, structural consistency multi-path verification is lacked, the self-adaptive rule generation capacity is weak, and a credible scoring mechanism does not have entropy perception discrimination capacity are solved.

The invention discloses a lane attribute making system, a lane attribute making method and a computer program product. The lane attribute making system comprises a map element encoder and a large language model. An output layer of the map element encoder is connected to an input layer of the large language model, the map element encoder is used for processing a vectorized map to output a vector encoding result, the vectorized map uses vector features to represent map elements, and the map elements comprise lanes; the input layer of the large language model further receives at least one of the image coding result and the text coding result, and the large language model is configured to generate lane attribute data according to at least one of the image coding result and the text coding result and the vector coding result. According to the scheme provided by the embodiment of the invention, the data of various modes including the vector mode can be processed, so that the driving rules are matched to the corresponding lanes, and accurate and detailed lane attribute data are provided for constructing the traffic rule layer.

The invention provides a privacy protection decision tree model reasoning method based on partial homomorphic encryption, and relates to the technical field of data privacy protection, and the method comprises the steps: obtaining to-be-predicted sample data, carrying out the preprocessing of each participant, and converting a ciphertext permutation matrix into a secret share matrix; performing bit decomposition on the to-be-predicted sample data to obtain a decomposition matrix, selecting an input sharing mode of the decomposition matrix according to the source of the to-be-predicted sample data, and then converting the input obtained decomposition matrix into a secret sharing share form; each participant performs comparative calculation by using the secret sharing share and the threshold share, evaluates Boolean functions of all leaf nodes to obtain indication result vectors, and randomly arranges the indication result vectors by using a secret share matrix to generate comparative result shares; and each participant constructs a vector pointing to a prediction output leaf node by using the comparison result share to obtain a prediction result, and outputs the prediction result to the sample data provider according to the to-be-predicted sample data source.

The application provides an automatic disk IO method based on structural mechanics CAE simulation software, comprising the following steps: S1, constructing a memory-disk cooperative computing framework, and dynamically monitoring a data access state in a simulation solving process; S2, automatically exchanging non-active core data structures to a high-performance disk according to a data access frequency, a life cycle and a memory pressure; S3, loading back to the memory on demand when subsequent calculation needs to access the data exchanged to the high-performance disk; wherein the core data structures comprise a stiffness matrix, shape function data, an intermediate result vector and derivative physical quantities, and the exchange process is transparent to the solver. According to the application, the exchange process is highly transparent to the core computing logic of the solver, is automatically managed by the system, and does not need user intervention, so that efficient utilization of memory resources and stable operation of super-large-scale model simulation are realized.

This application provides an address comparison and checking method, scalar processor, and device for a compressed issue queue. The method acquires a first instruction and a second instruction from the compressed issue queue; and obtains an address comparison and checking result based on the address correlation between the first instruction and each second instruction. Furthermore, a comparison and checking result vector is formed, and this vector is updated in advance based on the instruction status in the compressed issue queue in the next cycle. This method, by obtaining the address comparison and checking result based on the address correlation between the first instruction and each second instruction, can flexibly support diverse memory access granularities with a small hardware area overhead while ensuring processor performance (timing). Additionally, by calculating the next state of the comparison and checking result in advance based on the enqueue / dequeue status of the issue queue determined in the current cycle, and updating it directly in the next cycle, it ensures a one-to-one correspondence between the address comparison and checking result and the new instruction position in the issue queue, while also optimizing timing.

The invention discloses a high-precision coordinate calculation method based on Matlab, and belongs to the field of coordinate calculation technology and data processing, and the method comprises the steps: firstly, obtaining the combination of all points and vectors through a specific combination generation function, and then initializing a result vector; aiming at each combination, calculating intersection point coordinates and parameters by virtue of an accurate geometric intersection point calculation function, storing the intersection point coordinates and parameters in a result vector, and distributing the intersection point coordinates and parameters to a working area according to a rule by virtue of a working area assignment function; then, a midpoint coordinate matrix is obtained by applying a midpoint calculation function based on the coordinates of the intersection points, the distance between the points is calculated, a clustering center is determined by adopting a clustering algorithm, points of an intersection point concentrated area are screened out according to distance characteristics in a cluster, and finally, high-precision coordinates are calculated through a weighted average calculation function in combination with priori knowledge and weights. According to the method, the coordinate calculation precision can be effectively improved, good noise immunity is achieved, priori knowledge can be fully fused, and the method is suitable for numerous fields with high coordinate precision requirements.

To provide a grouping device for graded answer sheets in a written examination, which contributes to suppressing variations in the grade results. [Solution] The grouping device for graded answers in a written exam includes a reading unit that reads graded answer data in a written exam, a morphological analysis unit that performs morphological analysis on the answer data, a vectorization unit that vectorizes the results of the morphological analysis into answer sentence vectors, a grouping implementation unit that groups answer data that matches the rule definition and groups answer data that does not conform to the rule definition based on the answer sentence vectors to generate groups, a group feature analysis unit that analyzes statistical information on the features of the generated groups, a scoring result analysis unit that analyzes statistical information on the scoring results of each group, and an output unit that outputs statistical information on the group features and statistical information on the scoring results.

The present invention discloses a fusion positioning method. Based on the three-dimensional position vector, Doppler velocity measurement result vector, and position change value vector of the GNSS module positioning results, the differential pressure value results based on the barometer, and the pedestrian relative position vector based on the accelerometer, the three-dimensional position vector, Doppler velocity measurement result vector, position change value vector, differential pressure value results, and pedestrian relative position vector are fused on a cloud server using Kalman filtering to obtain high-precision positioning information, forming a unified data processing method, and capable of providing accurate position information of personnel in real time. In short, the present invention has the advantages of advanced algorithm, high stability, and high precision.

Efficient scaling of in-network compute operations to large numbers of compute nodes is disclosed. Each compute node is connected to a same plurality of network compute nodes, such as compute-enabled network switches. Compute processes at the compute nodes generate local gradients or other vectors by, for instance, performing a forward pass on a neural network. Each vector comprises values for a same set of vector elements. Each network compute node is assigned to, based on the local vectors, reduce vector data for a different a subset of the vector elements. Each network compute node returns a result chunk for the elements it processed back to each of the compute nodes, whereby each compute node receives the full result vector. This configuration may, in some embodiments, reduce buffering, processing, and / or other resource requirements for the network compute node or network at large.

The invention discloses a water supply pipe network water demand prediction method based on multi-source data fusion and a hybrid model. The hybrid model comprises a clustering model, a daily type classification model and a prediction model. The method mainly comprises the following steps: collecting multi-source data including historical water consumption data, meteorological data and holiday and festival information data, and preprocessing the historical water consumption data and the meteorological data; performing feature extraction by using a clustering method, and forming a clustering result vector; respectively making a feature matrix and an output label for training a classification model and a prediction model in combination with the multi-source data and the clustering result, and training a daily type classification and water demand prediction model by using an XGBoost algorithm; using the trained day type classification and water demand prediction model to predict the water demand at a certain moment in a certain day in the future, and finally obtaining a water demand prediction value at a corresponding moment of a to-be-predicted day. According to the method, the capability of the XGBoost model can be better played, and the method has good robustness and has a good prediction effect on different types of DMA.

The present application provides a method, device and server for risk assessment of hydrogen-doped pipeline leakage accidents. The method includes: constructing a Bayesian network for hydrogen-doped pipeline leakage accidents and determining the parameters of the basic events of each node in the Bayesian network; after obtaining the parameters of all nodes based on the parameters of the basic events of each node, constructing a risk index system for hydrogen-doped pipeline leakage accidents; constructing a judgment matrix according to each index in the risk index system for hydrogen-doped pipeline leakage accidents and solving the weights of each index in the index system; after passing the consistency test of the judgment matrix, calculating the result vectors of each index according to the weights of each index, and obtaining a comprehensive result vector based on all the index result vectors; after determining the safety level of the hydrogen-doped pipeline leakage accident according to the comprehensive result vector or membership degree, determining the risk level of the hydrogen-doped pipeline leakage accident, so that the risk of the hydrogen-doped pipeline leakage accident can be quantitatively evaluated, which is beneficial to risk prevention and control and accident emergency treatment.

The invention relates to the technical field of power equipment state monitoring and intelligent operation and maintenance, and discloses a high-voltage cable operation state reliability evaluation method and system. The method at least comprises the following steps: converting a standardized multi-modal data set into a standardized evaluation data set by referring to a pre-constructed integrity evaluation system, and generating a training sample set and a verification sample set based on the standardized evaluation data set; constructing a composite neural network architecture comprising a feature extraction layer, a nonlinear transformation layer and a parameter optimization layer, and training the composite neural network architecture based on the training sample set to obtain an evaluation result vector comprising a plurality of reliability dimensions; and comparing the evaluation result vector with a verification sample set from a plurality of dimensions including numerical value consistency check, trend goodness of fit analysis and anomaly detection capability evaluation to obtain a structured evaluation report reflecting the reliability of the running state of the high-voltage cable. The intelligent operation and maintenance response efficiency of the high-voltage cable is remarkably improved.

The application discloses an AI scoring report intelligent generation and analysis method based on cloud cooperation, which comprises the following steps: step one, obtaining original scoring data of an object to be evaluated; step two, performing data preprocessing on the original scoring data; step three, obtaining a scoring feature tensor and a collaborative scoring feature sub-tensor set through decryption verification and feature construction; step four, generating a scoring prediction result vector through an improved Crossformer network based on the scoring feature tensor and the collaborative scoring feature sub-tensor set; step five, generating a structured AI scoring report text through template matching and field semantic mapping based on the scoring prediction result vector; step six, performing semantic consistency detection and logical conflict detection on the structured AI scoring report text; and step seven, updating the improved Crossformer network. The Crossformer network is used, and the credibility of AI scoring report intelligent generation is improved.

This disclosure provides systems, methods and apparatuses for encoding a channel. The method includes mapping a plurality of information bits and a plurality of frozen bits to a vector based on a reliability order. The method includes generating a resulting vector by multiplying the vector by a transformed matrix to. The transformed matrix is defined by: a first identity matrix with a size equal to a first quantity of frozen bits in the vector that are located before a first information bit in the input vector, an upper triangle matrix, and a second identity matrix with a size equal to a second quantity of information bits in the vector that are located after a last frozen bit in the input vector. The method includes multiplying the resulting vector by a polar encoder matrix.

This application provides a text correction model training method and device, relating to the field of artificial intelligence technology. The text correction model training method includes: acquiring training data, which includes random erroneous text samples, near-phonetic erroneous text samples, and near-shape erroneous text samples; determining the input vector corresponding to each character in the training data, which includes a character representation vector, a positional representation vector, a pinyin representation vector, and a character shape representation vector; and training a pre-trained language model based on the input vector to obtain a text correction model. The text correction method includes: inputting the input vector corresponding to each character in the text to be corrected into the text correction model to obtain a text correction prediction result vector; and decoding the text correction prediction result vector to obtain the corrected target text. This application can not only reduce the cost of text correction but also improve the efficiency and accuracy of text correction.

The present invention belongs to the technical field of information security, and discloses a privacy-preserving decision tree evaluation method based on packed homomorphic encryption, including: obtaining a cryptographic algorithm, a public key, and a private key, processing and encrypting an original feature vector; after confusing a decision tree model, extracting a model structure index and an encoded model structure, and extracting ciphertext feature values from the encrypted feature vector based on the model structure and the cryptographic algorithm; based on the model structure and the cryptographic algorithm, performing a comparison between the feature values and the thresholds of the nodes of the decision tree model under ciphertext to obtain an encrypted comparison vector; based on the model structure and generating a path evaluation result; generating an encrypted evaluation result vector and related confusion information after confusion; obtaining an evaluation value of the original feature vector based on the encrypted evaluation result vector and the related confusion information. The present invention takes into account both privacy protection and computational efficiency, and has high model applicability.

This invention relates to an improved spatially smoothed source angle estimation method and apparatus based on a coprime linear array. The method includes: acquiring a signal from a source under test using an augmented coprime array to obtain a received signal; vectorizing the covariance matrix of the received signal, sorting the resulting vectors according to the element positions of the uniform linear array, and processing them according to a data processing strategy to obtain a virtual signal received by a virtual array; uniformly dividing the virtual array into overlapping virtual subarrays based on a pre-constructed spatial smoothing rule, and calculating the spatial smoothing covariance matrix of the received signals from the virtual subarrays; performing eigenvalue decomposition on the spatial smoothing covariance matrix using a predefined spatial spectrum estimation method to obtain a spatial spectrum function; and performing spectral peak search on the spatial spectrum function to determine the source angle of the source under test. The method provided by this invention improves the array degrees of freedom and the accuracy of source angle estimation by constructing a larger number of virtual array elements.