173 results about "Encoding algorithm" patented technology

The invention discloses a sprint learning path optimization method based on an improved whale optimization algorithm. The problem that existing sprint path planning is unreliable is solved. The method comprises the following steps: constructing a total learning cost objective function; using a random key coding algorithm based on chaotic mapping to represent the learning paths in a sequence mode, and using serial scheduling to obtain a plurality of candidate sprint learning paths; introducing an adaptive weight mechanism, an examination driving offset item and a Gaussian disturbance item, and adopting an improved white whale optimization algorithm to carry out iterative optimization on the candidate sprint learning paths to obtain a plurality of iteratively optimized sprint learning paths; in any iterative optimization, if the current candidate sprint learning path violates a first repair relation or / and a time window or / and a daily learning duration budget in the learning path, performing feasibility repair on the current candidate sprint learning path; performing neighborhood local search on the iteratively optimized sprint learning path based on a forgetting risk index; and outputting an optimal sprint learning path.

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 relates to the technical field of data information retrieval, and discloses an efficient batch privacy information retrieval method and system based on OCD and GPU optimizing.The efficient batch privacy information retrieval method comprises the steps that firstly, a server is initialized, a database is subjected to bucket division through a PBC encoding algorithm based on the client query number, the database subjected to bucket division is obtained, and when a user conducts information retrieval, the user can retrieve privacy information in a batch mode; the method comprises the following steps of: finding a sub-bucket position of a retrieval request in a server at a client by using a PBC scheduling algorithm, carrying out homomorphic encryption on the retrieval request in each sub-bucket, compressing all the retrieval requests by using an OCD to obtain a retrieval ciphertext, sending the retrieval ciphertext to the server, and sending the retrieval ciphertext to the server according to the retrieval ciphertext provided by the client. The retrieval result in the ciphertext form is obtained and returned to the client side, the client side decrypts the retrieval result in the ciphertext form, and the required retrieval result in the plaintext form is obtained. According to the method, the GPU is used for parallel processing, computing resources in a real scene can be fully utilized, and the computing efficiency of privacy information retrieval is remarkably improved.

The invention discloses a three-dimensional light field content construction and editing method, which relates to the field of naked eye three-dimensional display, and comprises the following steps of: collecting multi-view images of a real scene through a camera, and constructing a unified three-dimensional space coordinate system; generating an initial three-dimensional scene model; performing adjustable content editing on each frame of multi-view image; updating the initial three-dimensional scene model through the edited image; the updated three-dimensional scene model is rendered through a shear type rendering method, and an image sequence with multi-view consistency is generated; and coding the generated image sequence into three-dimensional light field content by using a light field coding algorithm. According to the method, the real static scene can be efficiently converted into the three-dimensional light field content with a complete structure and a continuous visual angle through multi-visual-angle image acquisition, three-dimensional reconstruction and rendering processing, so that the method has stronger sense of space depth and reality, and the visual experience and the display effect of the three-dimensional light field content are remarkably improved.

Data compression with quantum-resistant intrusion detection, that measures in real-time the probability distribution of an encoded data stream and analyzes entropy characteristics across multiple bit-scale windows to detect both classical and quantum-generated intrusions. The system compares the probability distribution to a reference probability distribution and uses statistical algorithms to determine divergence between distributions while simultaneously analyzing entropy cascade patterns characteristic of quantum computing sources. When divergence exceeds configured thresholds or quantum-generated characteristics are detected, the system generates intrusion alerts identifying the threat type. The system comprises encoding and decoding machines, an intrusion detection engine that performs multi-scale entropy analysis, a codebook training engine that creates quantum-resistant codebooks using entropy-stratified training algorithms, and databases including a quantum signature database storing compression patterns of known quantum algorithms. The codebook training engine adaptively retrains encoding algorithms upon detecting new quantum patterns, maintaining system effectiveness against evolving quantum threats.

The invention provides a multi-precision floating point fusion multiply-add structure and a microprocessor architecture, and the structure is characterized in that in a first-stage assembly line, an input preprocessing module decomposes an input operand to obtain a sign bit, an index bit and a mantissa bit; the Booth encoder generates partial products through a radix-4 Booth encoding algorithm, and the tree array multiplier compresses the partial products into three groups; in the second-stage assembly line, the CSA4-2 compression adder compresses the mantissa bits after the three groups of partial products and shift alignment into two groups; the summator module sums the two groups of partial products to obtain a mantissa summation result; in the third-stage assembly line package, a leading zero detection module performs leading zero detection on the mantissa summation result, and performs index adjustment and mantissa adjustment to obtain a normalized result; and in the fourth-stage assembly line, rounding of floating point data is carried out. According to the application, the operation performance, the energy efficiency ratio and the adaptability of the floating point fusion multiply-add structure are improved, so that the high performance and the high energy efficiency of the microprocessor are balanced.

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 present invention provides a flexible LED vehicle-mounted display screen and a production process thereof. The production process includes the following steps: adopting an FPC as a basic material of the display screen main body, fixing the flexible screen on a mold, mixing a PU glue, degassing treatment, glue dripping, automatic degassing, curing, and collecting. The flexible screen is fixed on a mold tray by magnetic suction or vacuum, and an air pressure sensor, a camera, a solenoid valve and the like are used to intelligently process the glue dripping and degassing processes. A plurality of Bluetooth BLE is installed on an FPC for communication, an MCU with a DMA function is adopted, and data are compressed to 50% of original data by an LZSS coding algorithm, which greatly reduces the time for the flexible screen to send data and improves the user experience.

The invention provides a forward error correction capability diffusion method and a related device. The method comprises the steps that the forward error correction capacity of a first node is determined, the forward error correction capacity comprises a coding algorithm set, the maximum grouping duration and the maximum protection level, and the forward error correction capacity of the first node comprises a first coding algorithm set, the first maximum grouping duration and the first maximum protection level; packaging the first coding algorithm set, the first maximum grouping duration and the first maximum protection level into a first capability message; and if the first node has the directly connected downlink node, sending a first capability message to the downlink node, so that the downlink node determines the forward error correction capability of the first node based on the first capability message. The forward error correction capability of the node in the transmission network is diffused downstream through the message, so that the receiving end node can determine the forward error correction coding mode of the sending end node based on the message to execute corresponding decoding, and the bandwidth utilization rate and the computing resource utilization rate are improved.

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 a Tranformer network-oriented hybrid precision multiplier, which is applied to an FPGA (Field Programmable Gate Array), and is used for allocating a target high-resolution image according to the weight precision of the weight data of the current layer of the target Tranformer network, the feature map precision of the feature map data of the target high-resolution image and a preset data allocation rule. Determining a first quantity of weight data and a second quantity of feature map data of a single calculation cycle; according to the first quantity of weight data and the second quantity of feature map data, recoding the weight data and the feature map data after determining a target recoding algorithm to obtain weight coded data and feature map coded data; performing multiplication calculation on the weight coding data and the feature map coding data in a single calculation period to obtain an initial product result of the single calculation period; and decoding the initial product result, and merging the multiple sub-feature map data of all the calculation periods corresponding to the current layer of the target Transform network to obtain target feature map data. According to the method, the resource utilization rate is improved on the basis of ensuring the model precision.

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.

The invention provides a power equipment logic configuration method and system, a terminal and a storage medium, and the method comprises the steps: packaging a pre-obtained function component library, and obtaining a packaged function component library; calling corresponding packaging function components from the packaging function component library to generate a plurality of logic nodes, and constructing a logic execution flow chart through the plurality of logic nodes based on a preset logic task demand; coding the logic execution flow chart based on a preset compiling algorithm to obtain a logic execution coding file; and scheduling and executing a corresponding logic task based on the logic execution coding file to complete the logic configuration of the power equipment. The problem that in the prior art, a logic configuration platform relies on professionals to write bottom layer codes, and consequently the expansibility of a power equipment logic component platform is poor is solved. According to the method, after function splitting and logic recombination are carried out on the configuration components, the configuration components are converted into executable codes for task scheduling and execution, and the expansibility of a power equipment logic component platform is improved.

The application discloses a fine-tuning fault-tolerant method, device, medium and product of an intelligent question and answer model, relates to the technical field of artificial intelligence, and comprises the following steps: dividing a preset low-rank matrix pre-injected in an intelligent question and answer model into at least two target sub-matrices according to the influence degree of the matrix parameters on the model performance, and matching corresponding target coding algorithms for different target sub-matrices, so that hierarchical and differentiated data protection is realized, and resource waste caused by uniform intensity protection on all parameters is avoided. Moreover, the target sub-matrices are respectively subjected to coding calculation at a first preset time point and a second preset time point of any two adjacent training steps, and the two coding results are compared, so that a data damage event occurring to the preset low-rank matrix in a training process can be effectively and quickly detected, thereby preventing error pollution from spreading to the whole model, and the reliability of the system is improved. The scheme disclosed by the application can significantly reduce fault-tolerant overhead and guarantee the stability in the fine-tuning process of the model.

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 invention provides a lightweight storage method and system for time series data of the Internet of Things, and relates to the technical field of data storage and compression of the Internet of Things, and the method comprises the steps: collecting multi-mode time series data and mechanical vibration energy data generated during the operation of equipment of the Internet of Things; extracting vibration frequency characteristics from the mechanical vibration energy data, and generating a time sequence vibration signal based on the vibration frequency characteristics; performing space-time correlation on the time sequence vibration signal and the multi-modal time sequence data to identify a data activeness level; according to the data activeness level, performing sparse representation processing on the multi-modal time series data to obtain sparse representation data; and carrying out dynamic dictionary construction and coding on the sparse representation data by adopting a self-adaptive dictionary coding algorithm to generate compressed coding data, and carrying out hierarchical storage management on the compressed coding data to realize lightweight storage. According to the invention, the storage efficiency and the processing capability of the time series data of the Internet of Things edge device are improved.

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 discloses a protection method and device for sensitive information of a payment switching system, equipment and a medium, and belongs to the field of data protection. The method comprises the steps that a first payment message sent by an acquirer system is received, the first payment message comprises sensitive information, and the sensitive information comprises a payment card number and non-card-number information; generating a confusion factor by using a coding algorithm according to at least part of characters in the card number of the payment card; respectively processing the card number information and the non-card-number information of the payment card by using a confusion factor and a hash algorithm to obtain a first character string corresponding to the card number of the payment card and a second character string corresponding to the non-card-number information; and sending a second payment message comprising the first character string and the second character string to the payment switching system. According to the embodiment of the invention, the security of the sensitive information in the payment process can be improved.

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 application discloses a kind of based on deep learning's image light weight encoding method and system, belong to deep learning technical field.The application uses convolutional neural network to train deep learning model, identifies current comprehensive environment;Generator and discriminator are constructed, and using the training of opposition, generator and discriminator are alternately trained, the encoding optimization of current security monitoring image is carried out, and simplified image is generated;Simplified image is input into image encoding algorithm and is encoded, and first light weight encoding image is obtained;Multi-source data features are extracted, and multi-source data feature templates under different comprehensive environments are established, comparative analysis is carried out, and the matching degree of environment and template is obtained;The information retention of target area in first light weight encoding image after light weight encoding is determined;Based on information retention, first light weight encoding image is adjusted, and after adjustment, it is encoded according to selected image encoding algorithm, and final light weight encoding image is generated.

The invention discloses a natural disaster risk identification method and device, equipment and a storage medium. The method belongs to the technical field of artificial intelligence and is applied to natural disaster risk assessment in financial underwriting business. According to the invention, disaster and topographic data are fused through a spatial superposition algorithm. An insurance policy address is accurately converted through a geocoding algorithm, so that address point data can be effectively associated with spatial data. The high-risk address is preliminarily screened by calculating the distance between the address point and the water system and the peripheral elevation change in combination with threshold judgment. And carrying out risk grouping by superposing disaster attribute values. By generating a risk thermodynamic diagram and superposing the risk thermodynamic diagram to a space view, high-risk region clustering is visually presented. And finally associating the risk indexes to an underwriting process database. According to the method, accurate analysis of single-point and batch risks is realized, a visual result can be quickly generated, scientificity and high efficiency of an insurance institution in natural disaster risk assessment and underwriting services are effectively improved, and underwriting risks are reduced.

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.