87 results about "Encoding algorithm" patented technology

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention discloses a high-speed fixed-point multiplication circuit. The multiplication circuit is mainly composed of a multiplier coding module, a partial product generation module, a partial product compression module and a traveling wave carry adder module. The multiplier coding module is composed of a radix-4-Booth coding algorithm and an opposite number generation module, and is used for carrying out three-bit block coding on input multiplication data and generating an opposite number of a multiplicand in advance. The partial product generation module generates a plurality of groups of partial products with symbol extension according to the multiplicand coded signal. And the partial product compression module adopts an improved Wallace compression structure to perform layered compression on the partial product. And the traveling wave carry adder module sums the two groups of results output by compression and outputs a multiplication result. According to the invention, multiplication accumulation series can be reduced, the switching times of invalid signals in the circuit can be reduced, and the longest delay path in an operation link can be shortened, so that fixed-point multiplication which is high in speed, low in power consumption and more favorable for a comprehensive tool in structure is realized.

A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

The invention provides an AT command parameter dynamic encoding and decoding method and system based on prefix identifications, and relates to the technical field of communication, the method comprises an encoding process and a decoding process, the encoding process comprises the steps of predefining a special character set and encoding algorithms, and configuring a unique corresponding prefix identification for each encoding algorithm; performing character traversal analysis on each parameter of the AT command to be sent, and judging whether any character in the predefined special character set is included or not; if not, directly outputting the parameter as a plaintext parameter; if so, encoding the parameter according to an encoding algorithm corresponding to the parameter, and splicing a corresponding prefix identifier in front of the encoded data to form an encoding parameter; and assembling all the plaintext parameters and the coding parameters according to the AT command grammar, generating a target command character string, and sending the target command character string. According to the invention, unnecessary processing overhead can be reduced, the command transmission length is shortened, and both transmission efficiency and reliability are considered.

The invention discloses an electric energy meter operation state inspection and evaluation method and system, and relates to the technical field of electric power metering safety, and the method comprises the steps: collecting and preprocessing the electrical working data of an electric energy meter, obtaining the reference current and working current of the electric energy meter, extracting error features, and generating a current error signal; carrying out voiceprint collection on the electric energy meter, and capturing a voiceprint signal of the ammeter; converting the voiceprint signal into a discrete pulse sequence through a coding algorithm, and obtaining a standardized pulse sequence through dimension conversion and optimization compression; performing feature extraction on the standardized pulse sequence by applying a pulse neural network, and obtaining fault probability distribution through synaptic weight calculation and neuron state updating after obtaining pulse sequence features; the feedback control signal and the electric energy meter evaluation report are generated based on the fault probability distribution, and the problem of poor real-time performance of fault mode recognition is solved.

This invention provides a lossless compression and decoding method for depth images. Lossless compression includes: dividing sequential data into multiple independent image blocks according to predetermined partitioning rules; selecting a corresponding thread model based on the characteristics of the depth image; calling a thread based on the thread model and using a reversible encoding algorithm to independently encode each image block; wherein, at the start of encoding for each image block, the encoder state is reset so that the encoding of each image block does not depend on the data of the preceding image block; generating metadata containing position index and compressed data length information and assembling it with the encoded image block for output. Lossless decoding includes: reading the metadata and decoding at least one image block based on the metadata. Through the above methods, parallel encoding and decoding are achieved; precise positioning via physical offsets and random access and on-demand region decoding are supported, improving the processing response of high frame rate depth streams under low computing power environments while ensuring lossless restoration.

The application discloses a kind of based on graph coding's diagnosis and surgical matching check method and system, the method includes: obtaining the diagnosis data and surgical plan data of target object;Based on feature extraction algorithm, extract the diagnosis change feature and surgical step feature in the diagnosis data and the surgical plan data;Based on graph network coding algorithm, the diagnosis change feature and surgical step feature are encoded into corresponding node relationship graph;The node relationship graph is input into trained graph neural network to obtain the matching degree parameter of output diagnosis and surgical plan.It can be seen that the present application can realize accurate medical diagnosis and surgery matching evaluation based on graph structure feature association, improve the scientificity and adaptability of surgical plan selection, reduce the surgical risk and treatment deviation caused by mismatch between scheme and diagnosis.

The invention relates to a rolling bearing fault diagnosis method based on multi-sensor fusion and a GAF-CNN. The method comprises the steps of collecting a plurality of one-dimensional time sequence signals of a rolling bearing through a plurality of sensors; converting each one-dimensional time sequence signal into a corresponding two-dimensional time sequence diagram by adopting a Grubrum angle field image coding algorithm; inputting the plurality of generated two-dimensional sequence diagrams into a pre-established neural network model, and extracting a feature vector from each two-dimensional sequence diagram in parallel by the model so as to output a plurality of feature vectors; performing feature fusion on the plurality of output feature vectors to generate a fusion feature; and finally carrying out fault diagnosis on the rolling bearing based on the fusion feature. The invention aims to solve the technical problems of low diagnosis reliability caused by incomplete information of a single sensor and insufficient diagnosis precision caused by insufficient feature extraction when a one-dimensional time sequence signal is directly input into a neural network in the prior art.

The invention relates to the technical field of voice communication, in particular to a voice communication method and system in a wide-narrow band intercommunication environment, and the method specifically comprises the following steps: collecting a voice signal through audio equipment, converting the voice signal into a digital signal, and transmitting information through the Internet. Different coding algorithms and bit rates are selected in different environments, the quality and efficiency of voice transmission are improved, the bit rate of audio coding is adjusted according to the change of network bandwidth, and the packet loss rate is reduced by using a forward error correction technology; and the security in the voice data transmission process is protected by adopting an encryption technology. According to the invention, by converting the voice signal into the digital signal and compressing the digital signal, the voice communication system in the broadband environment can utilize network bandwidth resources more efficiently, and the delay of data transmission is reduced.

This invention discloses a multi-level index compression method, apparatus, and device for reducing image storage space, relating to the field of image processing technology. The method includes obtaining the total number of color categories of a security display icon, and calculating an index value representing each pixel in a single color based on the obtained total number of color categories and a preset encoding algorithm. The storage space occupied by the current security display icon is calculated by combining the index value with the current size of the security display icon, or by combining the index value with the current size of the security display icon and the region code of the target area, thereby achieving compression processing. This application significantly reduces the encoding bit width of pixels through multi-level encoding of image pixel data, effectively compressing the data volume of the security display icon and greatly reducing the data storage volume.

The embodiment of the application discloses a risk rule extraction method and a risk rule extraction device, relates to the field of computer data mining, can output multivariate risk rules, and can meet the needs of various risk control businesses. Including: acquiring N groups of sample data; any one group of sample data in the N groups of sample data comprises attribute values corresponding to M variables; N is an integer greater than or equal to 2, and M is an integer greater than or equal to 2; processing the N groups of sample data by using an automatic coding algorithm and an association rule algorithm to determine X variables; the risk coefficients of sample data corresponding to the X variables are greater than a first threshold value, and the association degrees of the X variables are greater than a second threshold value; the X variables belong to the M variables, and X is an integer less than or equal to M; determining a risk rule according to the X variables, and the risk rule is used for indicating conditions met by the X variables to cause a risk event.

The invention discloses a visual representation global modeling method and system based on feature extraction, and the method comprises the steps: processing input visual data through employing a visual vocabulary bag feature coding algorithm, generating a feature vector, and obtaining space-time continuous feature representation through the dimension reduction reconstruction of a space-time continuous visual autoencoder; pixel and semantic category mapping is established by using a pixel-level semantic feature mapping algorithm, a pixel-level semantic feature map is generated, and the step comprises the sub-steps of dimension analysis, model construction and the like; inputting the feature map into a high-dimensional visual representation intelligent analysis platform, and screening through sub-steps of segmentation, standardization, correlation analysis and the like to obtain high-dimensional screening features; and on the basis of the screening features, constructing and optimizing a global feature incidence matrix through a visual representation global modeling technology, and outputting a global model. The system comprises corresponding function units, the visual data global association rule is accurately captured, and adaptability and analysis accuracy in a complex scene are enhanced.

The application provides a time window-based time series data compression hierarchical storage method and system, relates to the technical field of time windows, and the method comprises the following steps: firstly, collecting the running time series data of an industrial device, dividing the time series data into data blocks according to fixed time windows, and extracting statistical features of each data block to generate working condition feature data; then, analyzing the energy distribution features of the data blocks, generating optical pulse coding sequences through photoelectric pulse conversion and pulse width modulation technology; determining the compression level through analysis of the pulse density distribution, and combining the working condition feature data to perform compression processing by using a corresponding coding algorithm; finally, distributing the data to different storage areas according to the compression level to complete hierarchical storage. The application improves the storage efficiency of industrial monitoring data and reduces the storage cost.

This invention discloses a method and system for identifying encrypted traffic based on spatiotemporal features and semantic alignment. The method first extracts the spatial and temporal feature sequences of the network flow, and uses a byte-pair encoding algorithm to convert the spatial packet length into discrete symbols. Then, the discrete symbols and temporal features are mapped to a high-dimensional space and fused together. A global spatiotemporal feature vector is extracted through a network using a concatenated one-dimensional convolution and multi-head self-attention mechanism. Next, the text semantic bullseye matrix of fine-grained behaviors of various known applications is obtained offline from a large language model. Finally, the similarity between the spatiotemporal features and the text bullseye is calculated, and a multi-instance learning max-pooling mechanism is introduced for dynamic routing. Based on this, a contrastive learning loss function optimization model is constructed or cross-modal inference is performed. This invention completely overcomes the conceptual drift problem caused by changes in encrypted features, achieving extremely high generalization accuracy and feature interpretability across generations.

The invention discloses a shallow low-temperature hydrothermal deposit modeling method based on short-wave infrared, and relates to the technical field of artificial intelligence mineral product prediction, and the method comprises the steps: carrying out the weighted normalization processing of multi-source heterogeneous geological data, mapping the multi-source heterogeneous geological data to a unified feature space through a nonlinear fusion coding algorithm with a weight coefficient, and carrying out the modeling of the multi-source heterogeneous geological data. Generating a fusion feature vector; constructing a self-attention mechanism perceived by a geological structure, introducing a geological prior matrix reflecting terrain gradient and geochemical abnormal distribution on the basis of a key, query and value matrix, and dynamically adjusting local geological coupling weight parameters in an attention calculation process; and establishing a geological consistency loss mechanism, performing spatial constraint on the attention response and the alteration label, forming a composite loss structure through a nonlinear amplification term, a logarithmic penalty term and a smooth boundary constraint term, and enabling the geological consistency loss mechanism, classification loss and a regular term to participate in optimization together.

In some implementations, a user device may receive input that triggers transmission of information via sound. The user device may select an audio clip based on a setting associated with the device, and may modify a digital representation of the selected audio clip using an encoding algorithm and based on data associated with a user of the device. The user device may transmit, to a remote server, an indication of the selected audio clip, an indication of the encoding algorithm, and the data associated with the user. The user device may use a speaker to play audio, based on the modified digital representation, for recording by other devices. Accordingly, the user device may receive, from the remote server and based on the speaker playing the audio, a confirmation that users associated with the other devices have performed an action based on the data associated with the user of the device.

The application provides a data cache processing method, a cloud server and network equipment. The data cache processing method applied to the cloud server comprises the following steps: receiving service request information selected by a user and sent via the network equipment and network configuration parameters determined by the network equipment; determining service configuration parameters by selecting an encoding algorithm and an encoding strategy according to the service request information and the network configuration parameters; dividing cloud-encoding image frames into key frames and non-key frames, and calculating the inter-frame relationship of the key frames and the non-key frames according to the service configuration parameters; and sending the information of the calculated inter-frame relationship as configuration information for configuring cache to a terminal device via the network equipment.

The application relates to a multiplier based on radix-4 Booth coding and an improved Wallace compression tree, comprising a partial product generation module: input multipliers are re-encoded into a plurality of partial products by adopting a radix-4 Booth algorithm to form a compact partial product array; a partial product compression module: by utilizing an improved Wallace tree compression structure, the partial products are quickly compressed by two compressors to reduce the calculation path delay; and a final summation module: the compressed partial products are summed by using a high-speed adder to obtain a final multiplication result. The radix-4 Booth coding algorithm is adopted to make the partial product array arrangement regular, the circuit area is reduced, a new tree compression structure in which 3-2 compressors and 4-2 compressors are mixed is adopted to realize the Wallace tree, the compression efficiency is improved, and the key delay is reduced.

The embodiment of the invention provides a cloud file query method and related equipment, and belongs to the technical field of data query. The method comprises the steps of performing encryption processing on a first service pool and a first total pool through a preset encryption algorithm to obtain a corresponding second service pool and a second total pool; wherein the preset encryption algorithm comprises a reversible coding algorithm; constructing a preset bloom filter through a preset non-encrypted hash function according to the second total pool; comparing and analyzing the second business pool through the preset Bloom filter, and determining a target file identifier; wherein the target file identifier comprises a file identifier which exists in a second service pool and does not exist in a second full-amount pool; performing decryption processing on the target file identifier to obtain a target query result; wherein the decryption processing corresponds to the encryption processing. According to the embodiment of the invention, the efficiency and accuracy of master-free file checking can be effectively improved, and support is provided for performance optimization of cloud storage services.

This invention relates to the field of computer software technology. Specifically, it relates to a method for constructing a software development model adapted to multiple hardware platforms. When the target hardware platform is first started, a hardware baseline detection daemon is run to collect multi-dimensional low-level physical feature parameters and uses a sparse coding algorithm to fuse and generate a hardware baseline capability fingerprint. During the software development phase, a compile-time annotation processor is used to scan key functional modules and extract non-functional feature contract tags pre-embedded by the developer using structured declarative annotations into a tag list index file. After the fingerprint generation is completed upon software startup, a multi-dimensional constraint solving rule engine is used to perform conflict detection and optimal fit scoring and sorting of the module contract tags and hardware fingerprints, and to select the uniquely matching target functional module entry class. Based on this entry class, a server-side dynamic orchestration and distribution mechanism is used to extract only the associated high-cohesion atomized payload packages and inject them into the main program runtime context to complete dynamic assembly.

Controlling a bit rate for encoding a signal. The signal is encoded using at least two different encoding algorithms. A total bit rate is allocated to at least two components of the signal. A first component of the signal is to be encoded using a first encoding algorithm. A second component of the signal is to be encoded using a second encoding algorithm.

The invention relates to the technical field of integrated circuit design, in particular to a processing circuit architecture supporting multi-calculation precision dynamic switching, which comprises a control port module, a partial product generation module, a symbol compression module, an addition compression tree module and a final adder module. And single-cycle dynamic switching of various precisions is realized. The partial product generation module adopts a Booth coding algorithm to split an operation vector, and cooperates with boundary symbol selection logic to solve the problem of symbol expansion and auxiliary bit overlapping; the symbol compression module compresses the redundant extension bits through a preset coding mode; the addition compression tree module is formed by cascading multiple stages of compressors and inserting carry blocking logic; the final adder module is composed of a plurality of carry lookahead adders, and the output bit width is dynamically controlled through blocking logic. The architecture optimizes the parallel operation efficiency and the resource utilization rate, adapts to the deep learning training and reasoning full scene, and has the advantages of real-time performance and low power consumption.

The existing counterfeit speech detection method has weak robustness in the re-encoding and noise mismatch scene, in order to improve the robustness of the existing method, the counterfeit speech detection research work puts forward the strategy of data augmentation to the training data set. However, the data augmentation strategy can increase the training data quantity, reduce the model training efficiency, and can only be used for known encoding algorithm and noise difference scene. The present application relates to the field of counterfeit speech detection, especially to the field of counterfeit speech detection for re-encoding and noise interference scene, specifically relates to a counterfeit speech detection algorithm and system based on subject filtering, mainly designs a subject signal filtering module based on the relationship between the human ear hearing masking effect and the signal-to-noise energy ratio, which can eliminate the part causing the distribution difference in the spectrogram feature, at the same time, without increasing the training data quantity, can improve the robustness of the model in the unknown encoding algorithm and noise difference scene, and has good universality.

The invention discloses a wind turbine generator health index construction and state monitoring method based on sparse dictionary distance. The method comprises the following steps: (1) collecting an original vibration acceleration signal of a wind turbine generator in a health state; (2) collecting a signal training sample, and combining a sparse coding algorithm and a dictionary learning algorithm to construct a reference health dictionary representing a healthy operation mode of the equipment; (3) preprocessing the real-time vibration signal, collecting a signal training sample, and obtaining a real-time feature dictionary in combination with a sparse coding algorithm and a dictionary learning algorithm; (4) calculating a sparse dictionary distance of the reference health dictionary and the real-time feature dictionary based on Euclidean distance measurement; (5) obtaining all sparse dictionary distance calculation results and constructing health indexes; and (6) a dynamic fault threshold value is set based on the statistical characteristics of the health stage, and the operation state of the wind turbine generator is evaluated. According to the invention, early faults in the equipment can be found earlier, accurate state monitoring of the wind turbine generator is realized, and earlier fault early warning capability is provided.

This invention relates to the field of highway pavement inspection technology, specifically a method for detecting the friction performance of highway pavements based on intelligent sensors. The method includes: collecting vibration spectra, surface texture depth sequences, and environmental meteorological readings using a multimodal intelligent sensor array deployed on the highway pavement to form a raw pavement dataset. A sparse coding algorithm optimized based on the projection characteristics of tribological feature subspaces is used to extract vibration feature vectors. These vectors are then reconstructed in three dimensions and fused with temporal features to obtain texture feature maps and pavement environmental state vectors. Multiple features are input into a multi-source fusion network to generate a comprehensive state representation. Finally, a deep convolutional neural network model decodes and outputs estimated friction coefficients and spatial distribution maps. Based on this, risk level assessments and maintenance recommendations are generated and uploaded to a management platform. This method accurately preserves friction-related features, enables collaborative analysis of multi-source data, fully reflects the spatial distribution of pavement friction, and improves the accuracy and completeness of the detection results.

The application discloses a compression method and device of seismic data, electronic equipment, storage medium and product. The method comprises the following steps: extracting pre-stack seismic data into common offset data; performing discrete wavelet transform on the common offset data to obtain transformed data; performing multi-level coding on the transformed data based on a multi-level tree set splitting coding algorithm to obtain compressed data, wherein the coding level number of the multi-level coding includes an integer from a high coding level number to a maximum coding level number, the maximum coding level number is the total number of thresholds that can be used to determine the importance of wavelet coefficients, and the high coding level number is an integer greater than 1 and less than the maximum coding level number. The above scheme realizes seismic data compression by performing multi-level coding on the wavelet domain data of the seismic data through the multi-level tree set splitting coding algorithm, can guarantee the compression quality of the seismic data, and starts positioning and coding of the data from the high coding level number, so that the total coding level number is reduced, and the compression efficiency of the seismic data is improved.

The application relates to a disease prediction method and device based on implicit knowledge enhancement, equipment and a medium. The method comprises the following steps: calculating a patient comprehensive representation according to historical medical records and static information representation, and training a backbone network by using the patient comprehensive representation; obtaining a patient representation matrix by using the backbone network, and screening K similar patient representations with the highest similarity to a target patient by using cosine similarity; constructing a medical time sequence heterogeneous graph according to a disease type and a relationship type, encoding the medical time sequence heterogeneous graph by using a heterogeneous graph encoding algorithm to obtain a medical time sequence heterogeneous graph representation; constructing a disease comprehensive representation by using the patient comprehensive representation, the similar patient representation and the medical time sequence heterogeneous graph representation, training a disease prediction model according to the disease comprehensive representation, and outputting a disease prediction result by using the trained disease prediction model. The application enhances patient modeling by using the collective wisdom of similar patients and the potential complex relationship between diseases, and improves the accuracy of disease prediction.

The invention relates to an aviation structure impact load non-negative sparse coding algorithm expansion identification method and system. The method comprises the following steps: acquiring a monitored aviation structure and making a data set comprising an impact load force signal and a response signal; constructing a non-negative sparse coding model based on an impact load dynamic model and non-negative sparse prior; constructing an iterative convergence threshold algorithm for solving the non-negative sparse coding model; constructing a depth algorithm expansion network based on the iterative convergence threshold algorithm; training the depth algorithm expansion network by using the data set to obtain a network model with optimal parameters; and performing an impact load identification test by using the network model with the optimal parameters, and outputting an impact load to be identified. The method has the advantages that the calculation speed is high, parameters do not need to be manually set, and good noise immunity is shown; the neural network provided by the invention has better interpretability in structural design.