6 results about "Pair matching" patented technology

A lane global car-following pair matching and extracting method of a video track dataset, the vehicle track dataset contains the track point information of each vehicle at each time on the collected road, and has an important role in the research of both macroscopic traffic phenomena and microscopic driving behaviors such as car-following and lane-changing. The method aims to establish a standardized car-following pair matching and extracting method, which can detect and classify the driving behaviors of each track in the track dataset, and can efficiently and quickly identify the car-following pairs in the car-following state within the length range of the entire lane, thereby laying a foundation for in-depth research on driving behaviors.

The application belongs to the technical field of question and answer matching, and provides a question and answer matching method and system based on question pair matching and question and answer pair matching. After obtaining a question, a preset question-question matching model is used to obtain question vector representation in the question and vector representation of questions in a preset question and answer library, and on this basis, the similarity between the question in the question and the question in the preset question and answer library is calculated to preliminarily screen the question and answer library. Then, a preset question-answer matching model is used to obtain question vector representation in the question and vector representation of answers in the screened question and answer library, and on this basis, the similarity between the question in the question and the answer in the screened question and answer library is calculated, and the question and answer pair with the highest similarity score is taken as the final matched question and answer pair of the obtained question. In the question and answer pair matching process, the two times of similarity calculation realize preliminary screening and final determination of the question and answer pair, improve the accuracy of the similarity calculation, and ensure the matching effect.

The embodiment of the invention provides a multi-mode retrieval method and device and electronic equipment. According to the embodiment of the invention, the method comprises the steps: obtaining a question-answer pair matched with a target question from cached question-answer pairs; then, a judgment model with a small model parameter quantity is introduced to be used for judging whether the answers in the obtained question and answer pairs are matched with the target questions or not, if yes, the target answers of the target questions can be directly determined based on the answers in the question and answer pairs, and if not, the user questions are further analyzed through an RAG large model to obtain the target answers. According to the method, the cached question and answer pairs are used for retrieval, the judgment model with the small model parameter quantity is combined for judgment, and the RAG large model with the large model parameter quantity is further introduced for analysis only under the condition that the retrieved question and answer pairs do not meet the requirements, so that the retrieval efficiency can be effectively improved, efficient retrieval is realized, and the retrieval efficiency is improved. And the calling frequency of the RAG large model can be reduced as much as possible, and resource consumption is reduced.

This disclosure relates to the field of artificial intelligence technology, and particularly to a question-answer pair matching model training method, question-answer pair matching method, and apparatus. The method includes: acquiring question-answer pair training data, wherein the question-answer pair training data includes question training data and answer training data; inputting the question-answer pair training data into a question-answer pair matching model, predicting the matching degree between the question training data and the answer data, and outputting a predicted matching probability, wherein the predicted matching probability represents the similarity between the question training data and the answer data; calculating a target loss function based on the predicted matching probability; and adjusting the model parameters of the question-answer pair matching model based on the target loss function to obtain a target question-answer pair matching model. This method can improve the accuracy of the answer data output by the model for question data.

The application provides a method for instruction fusion using a superscalar processor and related equipment. A decoding unit obtains first instruction control signals of multiple instructions to be executed, and sends the first instruction control signals to a fusion decoding unit. The fusion decoding unit determines, for any one of at least one instruction pair, that a first instruction in the any one instruction pair is an I-type instruction, a second instruction in the any one instruction pair is an I-type instruction or an R-type instruction, and judges whether there is an instruction pair matching the any one instruction pair in a plurality of preset instruction pairs. If there is, instruction fusion is performed under the condition that a source operand of the first instruction is equal to a destination operand of the first instruction and the destination operand of the first instruction is equal to at least one source operand of the second instruction, to obtain a fusion instruction. A fusion instruction execution unit executes the fusion instruction according to an operation logic of the fusion instruction.

The invention relates to a signal identification method based on a signal fingerprint hash function, and belongs to the field of radio signal identification. The method comprises the following steps: segmenting continuous signals sampled by a receiver into frames with the same length, setting an overlapping ratio between adjacent frames, and capturing transient communication characteristics; adding frequency disturbance and burst pulse interference to the continuous signals; adopting a hybrid convolution kernel to extract different features of the spectrum data; mapping the high-dimensional feature vector into a low-dimensional binary hash sequence as a signal fingerprint; hash sequences of the same or similar patterns are identified through communication pair matching. According to the method, on the basis of the hybrid convolution kernels, through differentiated convolution kernel design, multiple convolution kernels are mixed in one convolution operation, the advantages of multi-scale convolution are utilized, different features of spectrum data are extracted, and finally, multi-modal features are converted into low-dimensional binary Hash sequences to serve as signal fingerprints, so that efficient signal recognition is achieved.