4 results about "Semantic pattern" patented technology

This invention discloses a computer network security protection method and system based on data analysis, relating to the field of computer security technology. Targeting IT / OT converged factory networks, this invention aligns traffic, DNS, authentication, processes, topology, and industrial instructions into session chains, generating semantic patterns and semantic constraints. It enables baseline establishment based on semantic patterns, segmented output of evidence chains and risk scores, and the linkage of semantic enhancement parameters with acquisition frequency, rate limits, and micro-isolation range. This allows the control end to handle only the abnormal segments with minimal intervention, reducing false alarms and production disruption risks, and enabling closed-loop update strategies to be transmitted back.

The application provides a power transmission line naming recognition method based on a bidirectional enhanced nonlinear pulse nerve, and the core innovation is that a BiENSNP module is constructed to deeply simulate a dynamic information processing mechanism of a biological nerve system, the model is endowed with powerful basic representation capability, and is especially good at modeling complex nonlinear semantic patterns contained in text. The fusion architecture significantly improves the robustness and accuracy of the model in identifying entities in the power transmission line construction field (especially generative text), thereby laying a solid and good scalable technical foundation for constructing high-quality power transmission line construction knowledge base and other key application scenarios.

The application provides a large model KV cache multi-dimensional compression method and system for long text tasks, which comprises the following steps: sampling input samples capable of covering the context structure and semantic mode of a large language model from long text task related corpus to construct a calibration dataset; loading the pre-trained large language model into an inference framework, performing forward inference using the calibration dataset, extracting each layer of KV cache generated in the forward inference process, and storing it by level; performing singular value decomposition on the KV cache of each layer and calculating the energy proportion of the first r singular values to determine the low rank degree of the KV cache of the layer; performing joint compression in the rank dimension and the quantization dimension according to the determined low rank degree; and using the KV cache of all layers after joint compression for inference deployment. The application realizes efficient compression and precision maintenance of the large language model KV cache in long text tasks.

The application discloses a bilingual semantic neural network compiling system and method, relates to the technical field of deep learning, and can receive an ONNX model derived from multiple deep learning frameworks, automatically generate an MLIRScript representation of a Python static subset, and provide executable debugging and MLIR conversion functions in an execution semantic mode and a compiling semantic mode respectively; in the execution semantic mode, intermediate tensor values, shapes and numerical distributions can be observed in real time, and model debugging and verification can be realized; in the compiling semantic mode, a standard MLIR high-level dialect representation is generated through static analysis, the operator topology, tensor types, shapes and parameters are kept consistent with the original ONNX model, and a basis is provided for subsequent multi-hardware platform optimization. The application realizes a neural network compiling path with unified input of multiple front ends and efficient deployment of multiple back ends, solves problems, such as difficult debugging and verification, framework binding limitation and model semantic loss, in the prior art, and improves the debuggability and portability of deep learning models across frameworks and hardware.