85 results about "Code algorithm" patented technology

The invention provides a power distribution station house wireless communication method based on a cooperative coding algorithm, which comprises the following steps: data processing of dynamic environment adaptation: according to the real-time requirement of power distribution station house equipment data, segmenting original data into data blocks carrying priority labels, and dynamically generating a redundant coding strategy based on a real-time network state; network-aware cooperative transmission: transmitting data blocks and redundant information through multi-node cooperation, wherein a transmission path and a redundancy distribution proportion are dynamically adjusted according to network signal quality, node load and fault state; and fault-tolerant recovery of closed-loop feedback: the receiving end jointly decodes the data block and the redundant information, triggers a local retransmission instruction according to a decoding result, and feeds back the local retransmission instruction to the network architecture to optimize a subsequent transmission path.

The invention relates to the technical field of product test data management, and discloses a product test data storage and management system based on an MES system. The system comprises a dynamic configuration module which can use a self-adaptive parameter adjustment algorithm to configure data acquisition parameters according to experiment types; the closed-loop feedback module is used for binding experimental data with production batches and process parameters based on a real-time association algorithm; the two-stage verification module is used for detecting data exception through a double-layer cascade verification algorithm; and the intelligent storage module is used for generating hierarchical storage labels by using a multi-dimensional structured coding algorithm. In addition, the system integration module realizes data interaction with ERP and SCADA systems based on an interface adaptation algorithm. The system solves the problems that in a traditional data management mode, collection parameters are fixed, data relevance is poor, a verification mechanism is imperfect, storage and retrieval are inconvenient and the like, data can be efficiently collected, accurately associated, strictly verified and intelligently stored, and the enterprise data management level and production competitiveness are improved.

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 relates to an encrypted network traffic classification method based on a pre-trained large language model, and belongs to the technical field of encrypted network traffic classification. The method mainly comprises three stages: a pre-training stage: converting original encrypted network traffic data into a double-byte hexadecimal format through preprocessing, generating and optimizing a basic vocabulary by using a byte pair coding algorithm, then constructing a large language model, and obtaining a pre-training model through distributed training; in the retraining stage, the data is subjected to head byte shuffling processing, and the pre-training model is quickly retrained to improve the generalization ability. In the fine tuning stage, to-be-classified data is preprocessed to generate hexadecimal double-byte data with labels, and classification task training is performed by using the retrained model to obtain an encrypted network traffic classification fine tuning model and classification accuracy. Through the combination of pre-training and retraining and the fine tuning of the pre-training model by means of data classification, the efficient processing and accurate classification of the complex encrypted network traffic are realized.

The invention relates to an attachment certificate dynamic form processing method and system, and belongs to the technical field of enterprise financial certificate intelligent processing, and the method comprises the steps: receiving an attachment certificate uploaded by a user, and extracting metadata features such as a file size, a format type and a hash value to generate an attachment object; carrying out fragmentation storage on the object by adopting an Erase Coding algorithm, and generating a voucher type identification cue word based on metadata; analyzing the voucher through a hybrid recognition model fusing a convolutional neural network and a Transform encoder, and outputting structured data containing a voucher type identifier; mapping the data into a database entity and triggering a voucher life cycle state change event, and driving a business rule engine to dynamically generate a form mode definition associated with a voucher type; and in response to user query, generating an interactive form through a responsive rendering engine in combination with a voucher type, state and authority dynamic combination form mode and instance data.

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.

The invention relates to a robot adaptive decision-making method and system based on an artificial intelligence algorithm. The method comprises the following steps: carrying out dimension reduction processing on environmental data through a feature compression coding algorithm to obtain a compressed feature vector; constructing an annealing strategy space by adopting a simulated annealing algorithm, and generating a candidate strategy set; the global exploration layer generates an exploration strategy based on strategy disturbance and obtains an instant reward value through environment interaction based on the exploration strategy, and the local optimization layer updates a Q value function based on a thermodynamic gradient rule; and adjusting temperature parameters of the simulated annealing algorithm through the updated Q value function and the instant reward value, and outputting a final execution strategy through strategy selection and model parameter updating. According to the method, through the technical means of multi-source data fusion, simulated annealing and reinforcement learning cooperation, dynamic temperature adjustment and the like, the global optimality and dynamic adaptability of a robot decision strategy are remarkably improved, and the robustness in a complex environment is enhanced.

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 discloses an artificial intelligence model training method and system based on a block chain, and relates to the technical field of block chains, and the method comprises the steps: collecting physical position coordinate data, and converting the data into a position coding character string through a position coding algorithm; generating an adjacent target training node list according to a prefix matching rule of the position coding character string, and triggering the target training nodes through block chain transaction to create a local training group; the target training node initiates real-time training gradient exchange in the local training group; when the exchange fails, requesting historical gradient data associated with the position coded character string from a gradient bank; the artificial intelligence model is updated by using historical gradient data, and a new training gradient is generated after updating is completed; and storing the new training gradient to a partition position corresponding to the position coding character string. According to the method, the artificial intelligence model is updated through the historical gradient data associated with the position coding character string to generate the new training gradient, and self-recovery of model iteration in a communication interruption scene is realized.

The invention discloses a digital signal self-adjustment and switching path optimization system. The system comprises a signal feature sensing unit, a modulation coding decision unit, a path state monitoring unit, a path optimization decision unit, a signal transmission execution unit and a feedback closed loop correction unit. The signal feature sensing unit collects a digital signal multi-dimensional basic parameter and converts the parameter into a feature vector; the modulation and coding decision-making unit selects a modulation and coding mode according to the characteristic vector based on an adaptive modulation and coding algorithm; the path state monitoring unit monitors multi-dimensional parameters of a transmission path to construct an incidence matrix, and the path optimization decision-making unit evaluates the path by using a multi-path optimization model and outputs a selection instruction; the signal transmission execution unit processes and transmits the signal according to the instruction; and the feedback closed-loop correction unit compares the parameters before and after transmission, and feeds back deviation data to optimize subsequent processing. The system realizes adaptive modulation and coding of digital signals and dynamic optimization of a transmission path, and improves transmission efficiency and stability.

The technology disclosed relates to a system for identifying same or similar instances of source code. In particular, the technology disclosed relates to a data repository designed for storing information regarding one or more associations between an instance of source code and a semantic hash code value. Wherein, semantic hash code value being determined in accordance with a semantic hash code algorithm. Wherein, semantic hash code algorithm being designed for computing a quantification of a set of measured characteristics of instance of source code. Wherein, quantification of set of source code characteristics of instance of source code being represented by semantic hash code value. Wherein, semantic hash code value corresponding to instance of source code. Wherein, an association between an identity of instance of source code and semantic hash code value is stored into data repository.

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.

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 technology disclosed relates to a system for identifying same or similar instances of source code. In particular, the technology disclosed relates to a data repository designed for storing information regarding one or more associations between an instance of source code and a semantic hash code value. Wherein, semantic hash code value being determined in accordance with a semantic hash code algorithm. Wherein, semantic hash code algorithm being designed for computing a quantification of a set of measured characteristics of instance of source code. Wherein, quantification of set of source code characteristics of instance of source code being represented by semantic hash code value. Wherein, semantic hash code value corresponding to instance of source code. Wherein, an association between an identity of instance of source code and semantic hash code value is stored into data repository.

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.

Embodiments of the present disclosure provide a machine video feature encoding method, a decoding method, an apparatus, a device, a medium and a program product. The method converts a machine video feature group into a feature tensor graph group, and respectively performs two rounds of rearrangement on the feature tensor graph and each feature tensor in the feature tensor graph to generate a second feature graph. Meanwhile, the position change information of the rearrangement is recorded through a serial number matrix. Based on the second feature graph and the serial number matrix, a compressed code stream is obtained. Through the rearrangement process, the generated second feature graph is suitable for encoding and decoding by using a video coding algorithm, thereby saving bandwidth, improving transmission speed and reducing the cost of related processing without changing the existing video codec hardware system.

The invention relates to the technical field of information storage and identification, and discloses a dot matrix code generation and encryption method based on a convolutional neural network and a coding algorithm, and the method comprises a parameter initialization step, an invisible positioning construction step, a robustness coding step, a dynamic key encryption step, and a templated image synthesis step. According to the dot matrix code generation and encryption method based on the convolutional neural network and the coding algorithm, convolutional feature values are performed on an image through the convolutional neural network to generate positioning points of the image, and a positioning point matrix used for image alignment is generated based on the point size and the point distance; image positioning is achieved without depending on a traditional image frame or a positioning block, and the anti-counterfeiting performance and safety of the dot matrix code image are improved; and secondly, a convolutional feature value extracted by the convolutional neural network in real time is used as a dynamic key, and XOR encryption is performed on the dynamic key and the coded data, so that the original information cannot be recovered even if the dot matrix code image is copied.

The invention relates to the technical field of spatial data coding of a geographic information system, in particular to a geographic position coding algorithm based on layer-by-layer segmentation of a spatial spherical triangle, which comprises the following steps of: 1, segmenting an element spherical triangle; 2, converting geographic coordinates into space rectangular coordinates; step 3, segmenting spherical triangle coding positions layer by layer; 4, compressing the position code by using the character code; and 5, decoding the character code and analyzing the position code. According to the method, layer-by-layer segmentation of the spatial spherical triangle is utilized, the method has remarkable hierarchical scalability, coding result character strings corresponding to points with closer geographic positions have higher prefix similarity, nearest neighbor point query can be converted into longest common prefix matching, and the nearest neighbor point query efficiency is improved. In addition, the coded result has higher information security, and the geographic information data protection capability is improved.

The application discloses a lossless compression circuit, chip and equipment for pulse waveform data, wherein the lossless compression circuit comprises an input flow control module, a division module, an LZ77 compressor, a conversion module, a check module, a Huffman encoder and an output flow control module; the input flow control module is used for receiving input data flow and realizing flow control; the division module is used for dividing the input data flow into data blocks; the LZ77 compressor is used for realizing data compression of the LZ77 compression algorithm; the conversion module is used for converting the compression result into a symbol of Huffman coding; the check module is used for calculating the length len and the cyclic redundancy check code of the whole data flow; the Huffman encoder is used for realizing Huffman coding algorithm coding; and the output flow control module is used for outputting data according to a specified compression format. The application aims to realize the lossless compression of electroencephalogram signals by combining the Deflate algorithm with the LZ77 compression algorithm and the Huffman coding algorithm based on hardware, so as to improve the processing efficiency of the pulse waveform data of the electroencephalogram signals.

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 application relates to a video coding method, device, computer equipment and storage medium. The method comprises the following steps: obtaining a region of interest and a non-region of interest in a current video frame of a video stream; using a first coding strategy to code the region of interest, using a second coding strategy to code the non-region of interest, and outputting coding data; wherein the coding quality of the first coding strategy is higher than that of the second coding strategy. The application can effectively control the code rate fluctuation by optimizing the coding algorithm to perform smooth processing on the data packet, thereby reducing the jitter and congestion risk in network transmission, and especially improving the picture freezing problem caused by large I frame transmission delay in the real-time video transmission scene.

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.

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 invention discloses a multi-prover zero-knowledge proving method without prover communication, relates to the technical field of multi-prover zero-knowledge proving methods, solves the defect that provers need frequent communication in the multi-prover zero-knowledge proving method in the prior art, and comprises the following steps of: setting an algorithm: inputting a safety parameter lambda and a proposition P to be proved, the verifier outputs N certification keys {EK1,..., EKN} and a verification public key VKF; according to the coding algorithm, a proposition input u to be proved is input, and a verifier outputs a code Iu; according to the certification algorithm, a server index b and a bth certification key EKb are input, a code Iu is input, and a bth server outputs a bth proposition result and a bth certification pi (y, b); the verification algorithm comprises the steps of inputting a verification public key VKF, proposition input u, N proposition results and N certificates {pi (y, b)} (b = 1) N, if verification succeeds, outputting 1, and otherwise, outputting 0. The decentralized cooperation generation proof thoroughly solves the problem of communication dependence.

The method and system for compressing and transmitting audio data in the AI recording scenario provided by the present invention include: in the AI recording scenario of the recording device, obtaining pure audio data; performing audio content analysis on the pure audio data based on a deep learning model to obtain an audio content analysis result; according to the audio content analysis result, combining an adaptive bit rate adjustment strategy, and using a hybrid coding algorithm to encode the pure audio data to obtain preliminary encoded audio data; using the lame library to dynamically adjust compression parameters according to the hardware performance, storage space, and network bandwidth of the recording device, and compressing the preliminary encoded audio data to obtain compressed audio data; wherein, the compression parameters include a compression ratio; and transmitting the compressed audio data to a target device. In the present invention, the compression parameters are dynamically adjusted to overcome the defect that the current inability to adaptively adjust the compression parameters results in poor compression efficiency.

The invention discloses a GPU memory optimization method and system, and the method comprises the steps: obtaining the real-time change characteristics of a task load through obtaining the dynamic data flow information of the task load and an intermediate data generation rule; according to the real-time change characteristics of the task load, an optimization algorithm based on gradient descent is adopted to determine the distribution proportion and the reserved space of a memory pool, and adjusted memory pool configuration is obtained; extracting available memory block information from the adjusted memory pool configuration, and compressing the intermediate data with low access frequency by adopting a Huffman coding algorithm to obtain a compressed intermediate data storage unit; and if the access frequency of the compressed intermediate data storage unit is lower than a preset threshold value, temporarily storing the compressed intermediate data storage unit to a low-speed storage area, and reallocating memory resources according to the task priority to obtain an optimized memory allocation scheme. According to the method, the memory utilization rate is remarkably improved, the access delay is reduced, and efficient memory resource management is realized.

Embodiments of the present disclosure provide a watermark embedding method, a watermark extraction method and device. The watermark embedding method comprises the following steps: first, obtaining a to-be-processed field in a to-be-processed text and a watermark insertion length of the to-be-processed field; then, encoding identification information into watermark code based on a coding system of a homograph code table and a digital coding algorithm; determining a to-be-encrypted watermark corresponding to the watermark code based on the watermark insertion length and the watermark code; encrypting the to-be-encrypted watermark based on a key to obtain an encrypted watermark; and finally, encoding the encrypted watermark into the to-be-processed field based on the homograph code table to generate a target field with watermark code, wherein the watermark is embedded by using a control character of the homograph code table on the basis of a general webpage watermark, and only the underlying coding and display form of the to-be-processed field are changed, without any change to the text format and expression content, thereby improving the security of the to-be-processed text and the target field in the to-be-processed text.

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.

This invention proposes an unconditional image generation method and system based on deep predictive coding networks, comprising: step S1, optimizing the network structure to implement a predictive coding algorithm with skip connections; step S2, building a training framework for the co-evolution of the input and output ends based on the original predictive coding algorithm; and step S3, improving the original predictive coding algorithm by introducing a non-convex energy function optimization algorithm into the predictive coding algorithm. This invention avoids gradient vanishing, accelerates training speed, and optimizes generation quality. Deep backpropagation (BP) convolutional networks suffer from gradient reduction, a problem first addressed in ResNet with skip connections. Skip connections directly send information from lower layers closer to the input to higher layers closer to the output, facilitating rapid gradient calculation to support parameter updates. This invention achieves unconditional image generation based on the original predictive coding and improves the stability of numerical computation.

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.