3results about How to "Avoid missed identification" patented technology

This invention relates to a dual-stack network asset discovery method based on multi-protocol collaborative detection and intelligent fingerprint analysis; it includes the following steps: multi-source parallel acquisition: collecting network layer information of various terminal devices in the intranet through command line interface or application programming interface; data cleaning and standardization: after acquisition, cleaning and standardizing the raw data; target device identification and detection strategy generation: based on the cleaned address list and combined with device information, identifying the target device type and automatically generating corresponding detection parameters; multi-protocol asset detection packet sending: sending detection packets to target IPv4 and IPv6 addresses according to configuration parameters; response data parsing and fingerprint recognition: performing structured parsing and multi-dimensional recognition on the received response data; dual-stack network data fusion and statistical analysis: after fingerprint recognition, fusing and analyzing the IPv4 and IPv6 detection results and constructing a unified asset database.

This disclosure relates to the field of raw coal sorting technology, specifically to a material identification method, a material sorting method, a material identification device, and equipment. The material identification method includes: acquiring a target image of the material to be identified; determining the contour of the material to be identified based on the target image; inputting the target image into a target recognition model to determine multiple recognition boxes included in the target image and their corresponding material types; and determining the type of the material to be identified based on the contour, the multiple recognition boxes, and the corresponding material types. This method can avoid spatial misalignment problems, overcome the limitations of single features, and eliminate interference from impurities, thereby helping to improve the overall accuracy and reliability of material identification and enhancing the material identification's resistance to environmental interference.

The invention relates to the technical field of network security, in particular to a risk identification system based on text and image bimodal fusion, which is characterized in that for text modal information, enhanced coding is carried out on webpage texts by constructing a Chinese sensitive knowledge graph fusing voice, fonts and semantics so as to accurately identify variation sensitive words; webpage visual features are extracted from the image modal information and fused with an OCR text; a cross-modal attention alignment mechanism is introduced to realize deep semantic interaction and complementary verification of text and image features, and finally, risk classification is performed on the fused features, and a reliable result is output by adopting confidence coefficient calibration. According to the method, the identification precision of risks such as variation content and disguised secret words is remarkably improved, and the robustness and accuracy of risk identification are improved.