6 results about "Code algorithm" patented technology

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.

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

The application discloses a semantic tendency analysis system based on dynamic word segmentation and multi-round inquiry, comprising the following modules: a character-level embedding construction module, which is used for performing embedding coding and generating a word embedding sequence; a multi-granularity context coding module, which is based on a multi-granularity context coding algorithm and outputs a multi-granularity context feature set; a semantic fusion module, which is used for calculating the difference degree of the multi-granularity context feature set and learning the fusion weight, and generating a fused semantic vector sequence; a neighboring word boundary perception module, which is used for constructing a boundary judgment node, generating a boundary judgment feature vector, and mapping the boundary judgment feature vector into a boundary confidence sequence; a dynamic word segmentation module, which is used for constructing a dynamic threshold function, calling a multi-round inquiry mechanism, and outputting a structured word sequence; and a semantic tendency discrimination module, which is used for performing word vector mapping and context modeling, and outputting a sentiment label result and a corresponding confidence score. The application constructs a high-precision semantic tendency analysis system suitable for a complex context.

This invention discloses a GPU memory optimization method and system. By acquiring dynamic data flow information and intermediate data generation patterns of the task load, the real-time variation characteristics of the task load are obtained. Based on these characteristics, a gradient descent-based optimization algorithm is used to determine the memory pool allocation ratio and reserved space, resulting in an adjusted memory pool configuration. Available memory block information is extracted from the adjusted configuration, and a Huffman coding algorithm is used to compress low-access-frequency intermediate data, resulting in compressed intermediate data storage units. If the access frequency of the compressed intermediate data storage units is lower than a preset threshold, the compressed intermediate data storage units are temporarily stored in a low-speed storage area, and memory resources are reallocated according to task priority, resulting in an optimized memory allocation scheme. This invention significantly improves memory utilization, reduces access latency, and achieves efficient memory resource management.

The application discloses a multi-dimensional time series data lossy compression method based on dimension correlation grouping. The method extracts dynamic trend characteristics of each dimension data, calculates dynamic error boundary of each dimension, constructs high correlation dimension grouping and determines primary and secondary dimensions in each grouping; introduces error feedback mechanism in time domain to perform differential processing on the data, generates quantized differences of each dimension data; calculates differential residuals of secondary dimensions in each group relative to the primary dimension, adjusts compression strategy according to the amplitude characteristics of the residuals, and generates a to-be-encoded set composed of a primary dimension sequence and a secondary dimension residual sequence; and performs compression on the to-be-encoded set through an entropy coding algorithm and encapsulates the to-be-encoded set. The compression strategy of the application adopts correlation grouping between dimensions, feedback quantization, residual construction between dimensions in a group and bit reorganization, and significantly improves compression density and reconstruction accuracy for large-scale sensor array data.