124 results about "Code algorithm" patented technology

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

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 provides an inadvertent dynamic searchable encryption method, and relates to the technical field of information security, and the method comprises the steps: constructing a new Labeled PSI technology in combination with a VOLE technology based on a receiver processor and a sender processor in a bottom layer module, and obtaining an intersection Z of the two parties and a corresponding label; and in the application layer, the client and the server negotiate a database updating method together to dynamically update the database, and the client and the server negotiate a database searching method together to decrypt the document so as to complete encryption processing which can be searched for dynamically and accidentally. According to the invention, the security and efficiency of the dynamic searchable encryption technology are improved.

The invention discloses a method for generating a three-dimensional building GLTF file based on index compression. The method comprises the following steps: S1, collecting three-dimensional coordinate data of a building; s2, grouping and classifying the data according to floors to form a classification index structure based on a hash table; s3, selecting a reference model and recording Mesh data and vertex coordinates of the reference model; s4, calculating a translation matrix T of the polyhedral structure, and performing coordinate transformation; s5, generating Mesh data of other polyhedrons based on index compression, and reducing redundant surface data by adopting a topology preserving surface optimization algorithm; s6, independently processing the geometric data of each floor, and generating corresponding Mesh data and Node nodes; and S7, combining all floor data to generate a complete GLTF file, and performing compression by using a dynamic Huffman coding algorithm. Through the improved index compression and dynamic Huffman coding technology, the file size is remarkably reduced, the transmission efficiency is improved, and the geometric and topological integrity of the model is ensured.

The invention discloses a data processing method and a related device, and the method comprises the steps: firstly obtaining a target instruction of a virtual machine, and enabling the target instruction to be used for controlling the virtual machine; and then according to the target virtual memory address corresponding to the target instruction, an encryption key corresponding to the target instruction is determined, and different virtual memory addresses correspond to different encryption keys. And according to the encryption key, performing instruction coding on the target instruction through a coding algorithm to obtain a coding result, and storing the coding result as the target instruction in the target virtual memory address. Finally, in the running process of the virtual machine, when a calling request for the target instruction is obtained, the coding result is read from the target virtual memory address, instruction decoding is conducted on the coding result through a decoding algorithm corresponding to the coding algorithm according to the encryption key, and the target instruction is obtained. The encryption key is generated based on the storage address of the instruction, and the encryption key is used for instruction coding to realize the specificity of a coding result, so that the security of the virtual machine is improved.

The invention discloses a copper product quality defect traceability system and method. The system comprises a data acquisition module, a data processing module, a quality detection module and a defect traceability module. Relates to the technical field of copper product quality defect traceability, and solves the technical problem that quality defect information of a copper product is difficult to accurately obtain in the prior art. The method comprises the following steps: calculating a quality evaluation coefficient based on surface defect characteristics and performance data; integrating the quality evaluation coefficient and the basic information into traceability information; coding the traceability information based on a coding algorithm and storing the coded traceability information in a database; and performing defect traceability on the copper product based on the quality evaluation result and the traceability code. The performance standard-reaching rate is calculated according to the performance data and the preset performance standard, and the performance standard-reaching rate corresponding to each piece of performance data is multiplied by the corresponding proportionality coefficient to obtain the second evaluation coefficient, so that the performance of the copper product can be accurately reflected, and the quality defect tracing accuracy of the copper product can be improved.

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 provides a universal Transform acceleration method and system based on floating point instruction set extension, and the method comprises the steps: constructing a multiplier which comprises a plurality of stages of adders and a 4-Booth coding algorithm which are connected in sequence, and coding data; the coded data is operated through a plurality of stages of adders in sequence to obtain the output of a multiplier; the construction of the floating-point number register comprises the following specific steps: adding a floating-point number register group, generating the floating-point number register group, enabling input result register indexes to correspond to respective register numbers by writing port logic of the floating-point number register group, generating a writing enable signal, writing data into the register by an enabled general register, and writing the data into the floating-point number register group. The read port is realized by using a multi-path parallel selector, and a selection signal of the multi-path parallel selector is an index of a read operand register; and the RISC-V processor accelerates the operation of the Transform neural network based on the constructed multiplier and the floating-point number register.

The invention provides an exhaustion report generation method and device, a storage medium and electronic equipment, and is applied to the technical field of artificial intelligence. According to the method, the key attribute information is extracted by acquiring the basic information, the risk information and the transaction information of the target customer, and the knowledge set related to the attributes is found by using the pre-constructed knowledge base and the vector index database. And then, calculating the similarity between the key attributes and the associated knowledge by using a cross coding algorithm, and sorting according to the similarity to obtain the most relevant knowledge. And the sorting result is used for constructing cue words for inputting a large language model, so that a more accurate and practical exhaustion note report is generated. According to the method and the system, the accuracy and the practicability of the exhaustion report are remarkably improved by combining specific customer information and rich associated knowledge.

The invention relates to the technical field of point cloud compression coding, and discloses a point cloud projection video low-complexity coding optimization method based on depth structure prediction, which comprises the following steps of: 1, extracting an occupied video, a geometric video and an attribute video of a dynamic point cloud sequence under different code rates; step 2, extracting feature information of a near-layer frame to perform feature enhancement to obtain near-layer enhancement features # imgabs0 # corresponding to the occupancy graph, the attribute graph and the geometric graph; step 3, extracting feature information of a far-layer frame to perform feature enhancement to obtain near-layer enhancement features # imgabs0 # corresponding to the occupancy graph, the attribute graph and the geometric graph; 4, performing feature enhancement on a projection image block according to feature information of different types of frames, and realizing more accurate depth division structure prediction; and step 5, realizing low-complexity point cloud projection video coding acceleration by combining a depth division structure prediction result with an optimized coding algorithm.

The invention relates to the technical field of data processing, in particular to a remote data migration backup method and system, and the method comprises the steps: obtaining the temperature and current during the operation of a machine, and constructing a cooperative relation curve; and segmenting the collaborative relation curve by using the extreme point of the collaborative relation curve, and calculating the difference between the normality of any data in any segment and the average normality of adjacent data to obtain the anomaly index of the data. The interference degree of the data is calculated, the temperature or the current at the corresponding moment is corrected based on the interference degree, the correction direction is the direction close to the adjacent data, the correction amplitude is in positive correlation with the interference degree, and correction values of the temperature and the current are compressed through a Huffman coding algorithm so that migration backup can be conducted. According to the method, the data compression efficiency and the accuracy of the compression result in the data migration backup process can be improved.

The invention relates to a multiplier based on radix-4Booth coding and an improved Wallace compression tree, and the multiplier comprises a partial product generation module which recodes an input multiplier into a plurality of partial products through employing a Radix-4Booth algorithm, and forms a compact partial product array; the partial product compression module is used for quickly compressing the partial product through two compressors by utilizing an improved Wallace tree compression structure, and reducing the calculation path delay; and the final summation module is used for summing the compressed partial product by using a high-speed summator to obtain a final multiplication result. According to the invention, a radix-4Booth coding algorithm is adopted, so that a partial product array is regularly arranged, the circuit area is reduced, a new tree type compression structure in which a 3-2 compressor and a 4-2 compressor are mixed is adopted to realize a Wallace tree, the compression efficiency is improved, and the key delay is reduced.

The invention provides an audio data compression transmission method and system in an AI recording scene, and the method comprises the steps: obtaining pure audio data in the AI recording scene of recording equipment; 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 adopting a hybrid coding algorithm to code the pure audio data to obtain preliminarily coded audio data; dynamically adjusting compression parameters according to the hardware performance, the storage space and the network bandwidth of the recording equipment by using a time library, and compressing the preliminarily coded audio data to obtain compressed audio data; wherein the compression parameter comprises a compression ratio; and transmitting the compressed audio data to target equipment. According to the method, the compression parameters are dynamically adjusted, and the defect that the compression efficiency is poor due to the fact that the compression parameters cannot be adjusted in a self-adaptive mode currently is overcome.

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 discloses an adaptive modulation and coding method based on LSTM-DQN, and belongs to the field of meteor trail communication. Aiming at the problem of insufficient timeliness of channel state information caused by long transmission time delay of a meteoric trail communication channel and the problem that performance reduction of a traditional adaptive modulation coding algorithm in a fast time-varying channel is limited, the invention provides a method for carrying out strategy selection by combining an LSTM (Long Short Term Memory) and a DQN (Digital Quality Network) network so as to improve the throughput of a system. The method comprises the following steps that firstly, a network framework of LSTM and DQN is initialized, the transmission condition of each step is obtained through a signal-to-noise ratio estimation algorithm and error detection codes to serve as neural network training data, and then the LSTM is responsible for conducting time sequence prediction to improve the problem that channel state information is expired; the DQN network uses empirical playback and a target network to train an adaptive modulation coding policy network. Through continuous iteration of network parameters, selection of modulation and coding scheme strategies under different channel states is optimized, and then a modulation and coding scheme which is more suitable for the current state of a channel is obtained.

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 a code rate estimation method suitable for hardware implementation. The code rate estimation method comprises the following steps: S1, optimizing a context probability model; s2, independently designing a bit processing unit (BPU) for updating and storing a context model value; and S3, carrying out hardware design on the entropy coding process realized by hardware. According to the invention, a'context modeling of binarized bin 'module in the CABAC entropy coding process is optimized, and an algorithm and a hardware pipeline structure are provided, so that the time for estimating the consumed bit digital rate by using an entropy coding algorithm is shortened, the area and the power consumption of a hardware product are reduced, hardware implementation is facilitated, and the cost is reduced. According to the method, the dependency that context model values need to be updated bin by bin in each bin of entropy coding in the rate distortion optimization (RDO) process is eliminated, hardware parallel pipeline is facilitated, meanwhile, the key path of updating the context model values bin by bin is reduced, the calculation complexity is reduced, and thus the hardware power consumption is reduced.

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 relates to the technical field of underwater communication, discloses an online signal-level simulation transceiving system of an underwater mobile platform, and aims to improve the performance of an underwater communication system in a complex underwater environment, especially the instantaneity of submarine state monitoring and instruction receiving. The system integrates multi-mode communication, signal optimization and error correction technologies, and ensures that efficient and stable data transmission and information processing are realized in a variable underwater acoustic channel environment. By dynamically switching a low-speed transmission mode, a medium-speed transmission mode and a high-speed transmission mode and combining an error detection and correction mechanism of LDPC codes and a signal level simulation technology, a modulation and coding algorithm is optimized, and the anti-interference capability and the transmission efficiency of signals are improved. In addition, the system also introduces a quasi real-time and real-time communication combined mechanism to realize efficient communication of the underwater mobile platform. According to the invention, the communication efficiency and reliability are obviously improved, the adaptability of the system is enhanced, the real-time performance is ensured, and a powerful guarantee is provided for improving the underwater working efficiency.

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 embodiment of the invention provides a video conference communication method, device and system. The method comprises the following steps: collecting video stream data; calling a pre-integrated video stream coding algorithm to carry out coding processing on the video stream data to obtain video coding data; and sending the video coding data to a conference sending device through a video stream transmission channel, so that the conference sending device performs slicing and packaging processing on the video coding data, and sends a packaged video data packet to a conference receiving device. According to the embodiment of the invention, the purpose that CPU resources of a host are not used at all can be achieved, and the problems of hardware inconsistency and the like generated when the MediaCodec calls the GPU are avoided.

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for off-site data migration and backup. The method includes: obtaining the temperature and current during machine operation and constructing a collaborative relationship curve; using the extreme points of the collaborative relationship curve to segment the collaborative relationship curve, calculating the difference between the normality of any data in any segment and the average normality of adjacent data, and obtaining the anomaly index of the data. Calculating the interference degree of the data, correcting the temperature or current at the corresponding moment based on the interference degree, the correction direction is towards the adjacent data, the correction amplitude is positively correlated with the interference degree, and using the Huffman coding algorithm to compress the corrected values of the temperature and current for migration and backup. The present invention can improve the compression efficiency of data and the accuracy of the compression result during the data migration and backup process.

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.